Drug Design for CNS Diseases: Polypharmacological Profiling of Compounds Using Cheminformatic, 3D-QSAR and Virtual Screening Methodologies

The antiviral drug Ribavirin effectively modulates the amyloid transformation of α-Synuclein protein

α-Synuclein (α-syn) is an intrinsically disordered protein, linked genetically and neuropathologically to Parkinson's disease where this protein aggregates within the brain. Hence, identifying compounds capable of impeding α-syn aggregation puts forward a promising approach for the development of disease-modifying therapies. Herein, we investigated the efficacy of Ribavirin, an FDA-approved compound, in curtailing α-syn amyloid transformation, employing an array of bioinformatic tools and systematic analysis using biophysical techniques. Ribavirin shows a dose dependent anti-aggregation propensity where it effectively subdued the formation of mature fibrillar aggregates of α-syn, where even at the lowest concentration there was a 69 % reduction in the ThT maxima. Ribavirin averts the formation of mature fibrillar aggregates by interacting with the NAC domain of α-syn. Ribavirin redirects the amyloid transformation of α-syn by emanating aggregates of lower order with reduced cross β-sheet signature and revokes the formation of on-pathway amyloids. Collectively, our study puts forward the novel potency of Ribavirin as a promising molecule for therapeutic intervention in Parkinson's disease.

Unlocking the potential of signature-based drug repurposing for anticancer drug discovery

Cancer is the leading cause of death worldwide and is often associated with tumor relapse even after chemotherapeutics. This reveals malignancy is a complex process, and high-throughput omics strategies in recent years have contributed significantly in decoding the molecular mechanisms of these complex events in cancer. Further, the omics studies yield a large volume of cancer-specific molecular signatures that promote the discovery of cancer therapy drugs by a method termed signature-based drug repurposing. The drug repurposing method identifies new uses for approved drugs beyond their intended initial therapeutic use, and there are several approaches to it. In this review, we discuss signature-based drug repurposing in cancer, how cancer omics have revolutionized this method of drug discovery, and how one can use the cancer signature data for repurposed drug identification by providing a step-by-step procedural handout. This modern approach maximizes the use of existing therapeutic agents for cancer therapy or combination therapy to overcome chemotherapeutics resistance, making it a pragmatic and efficient alternative to traditional resource-intensive and time-consuming methods.

Therapeutic potential of stilbenes in neuropsychiatric and neurological disorders: A comprehensive review of preclinical and clinical evidence

Neuropsychiatric disorders are anticipated to be a leading health concern in the near future, emphasizing an outstanding need for the development of new effective therapeutics to treat them. Stilbenes, with resveratrol attracting the most attention, are an example of multi-target compounds with promising therapeutic potential for a broad array of neuropsychiatric and neurological conditions. This review is a comprehensive summary of the current state of research on stilbenes in several neuropsychiatric and neurological disorders such as depression, anxiety, schizophrenia, autism spectrum disorders, epilepsy, traumatic brain injury, and neurodegenerative disorders. We describe and discuss the results of both in vitro and in vivo studies. The majority of studies concentrate on resveratrol, with limited findings exploring other stilbenes such as pterostilbene, piceatannol, polydatin, tetrahydroxystilbene glucoside, or synthetic resveratrol derivatives. Overall, although extensive preclinical studies show the potential benefits of stilbenes in various central nervous system disorders, clinical evidence on their therapeutic efficacy is largely missing.

Synthesis and biological evaluation of novel 3-(5-substituted-1H-indol-3-yl)pyrrolidine-2,5-dione derivatives with a dual affinity for serotonin 5-HT1A receptor and SERT

The serotonin 1A (5-HT1A) receptors and serotonin transporter (SERT) are important biological targets in the treatment of diseases of the central nervous system, especially for depression. In this study, new 3-(1H-indol-3-yl)pyrrolidine-2,5-dione derivatives linked with the 3-(1,2,3,6-tetrahydropyridin-4-yl)-1H-indole moiety were synthesised and evaluated for their affinity for 5-HT1A receptor and serotonin reuptake inhibition. Selected compounds were then tested for their affinity for D2, 5-HT2A, 5-HT6 and 5-HT7 receptors, and also in in vitro metabolic stability assays in human microsomes. Finally, in vivo assays allowed us to evaluate the agonist-antagonist properties of pre- and postsynaptic 5-HT1A receptors. 3-(1-(4-(3-(5-methoxy-1H-indol-3-yl)-2,5-dioxopyrrolidin-1-yl)butyl)-1,2,3,6-tetrahydropyridin-4-yl)-1H-indole-5-carbonitrile (4f) emerged as the most promising compound from the series, due to its favourable receptor binding profile (Ki(5-HT1A) = 10.0 nM; Ki(SERT) = 2.8 nM), good microsomal stability and 5-HT1A receptor agonistic activity.

Discovery of Small Molecule PARKIN Activator from Antipsychotic/Anti-neuropsychiatric Drugs as Therapeutics for PD: an In Silico Repurposing Approach

Although there is presently no cure for Parkinson's disease (PD), the available therapies are only able to lessen symptoms and preserve the quality of life. Around 10 million people globally had PD as of 2020. The widely used standard drug has recently been revealed to have several negative effects. Additionally, there is a dearth of innovative compounds entering the market as a result of subpar ADMET characteristics. Drug repurposing provides a chance to reenergize the sluggish drug discovery process by identifying new applications for already-approved medications. As this strategy offers a practical way to speed up the process of developing alternative medications for PD. This study used a computer-aided technique to select therapeutic agent(s) from FDA-approved neuropsychiatric/psychotic drugs that can be adopted in the treatment of Parkinson's disease. In the current work, a computational approach via molecular docking, density functional theory (DFT), and pharmacokinetics were used to identify possible (anti)neuropsychiatric/psychotic medications for the treatment of PD. By using molecular docking, about eight (anti)neuropsychiatric/psychotic medications were tested against PARKIN, a key protein in PD. Based on the docking score, the best ligand in the trial was determined. The top hits were compared to the reference ligand levodopa (L-DOPA). A large proportion of the drugs displayed binding affinity that was relatively higher than L-DOPA. Also, DFT analysis confirms the ligand-receptor interactions and the molecular charge transfer. All the compounds were found to obey Lipinski's rule with acceptable pharmacokinetic properties. The current study has revealed the effectiveness of antineuropsychiatric/antipsychotic drugs against PARKIN in the treatment of PD and lumateperone was revealed to be the most promising candidate interacting with PARKIN.

Dopamine Receptor Ligand Selectivity-An In Silico/In Vitro Insight

Different dopamine receptor (DR) subtypes are involved in pathophysiological conditions such as Parkinson's Disease (PD), schizophrenia and depression. While many DR-targeting drugs have been approved by the U.S. Food and Drug Administration (FDA), only a very small number are truly selective for one of the DR subtypes. Additionally, most of them show promiscuous activity at related G-protein coupled receptors, thus suffering from diverse side-effect profiles. Multiple studies have shown that combined in silico/in vitro approaches are a valuable contribution to drug discovery processes. They can also be applied to divulge the mechanisms behind ligand selectivity. In this study, novel DR ligands were investigated in vitro to assess binding affinities at different DR subtypes. Thus, nine D2R/D3R-selective ligands (micro- to nanomolar binding affinities, D3R-selective profile) were successfully identified. The most promising ligand exerted nanomolar D3R activity (Ki = 2.3 nM) with 263.7-fold D2R/D3R selectivity. Subsequently, ligand selectivity was rationalized in silico based on ligand interaction with a secondary binding pocket, supporting the selectivity data determined in vitro. The developed workflow and identified ligands could aid in the further understanding of the structural motifs responsible for DR subtype selectivity, thus benefitting drug development in D2R/D3R-associated pathologies such as PD.

Designing multi-target drugs for the treatment of major depressive disorder

INTRODUCTION

Major depressive disorders (MDD) pose major health burdens globally. Currently available medications have their limitations due to serious adverse effects, long latency periods as well as resistance. Considering the highly complicated pathological nature of this disorder, it has been suggested that multitarget drugs or multi-target-directed ligands (MTDLs) may provide long-term therapeutic solutions for the treatment of MDD.

AREAS COVERED

In the current review, recent lead design and lead modification strategies have been covered. Important investigations reported in the last ten years (2013-2022) for the pre-clinical development of MTDLs (through synthetic medicinal chemistry and biological evaluation) for the treatment of MDD were discussed as case studies to focus on the recent design strategies. The discussions are categorized based on the pharmacological targets. On the basis of these important case studies, the challenges involved in different design strategies were discussed in detail.

EXPERT OPINION

Even though large variations were observed in the selection of pharmacological targets, some potential biological targets (NMDA, melatonin receptors) are required to be explored extensively for the design of MTDLs. Similarly, apart from structure activity relationship (SAR), in silico techniques such as multitasking cheminformatic modelling, molecular dynamics simulation and virtual screening should be exploited to a greater extent.

The medicinal chemistry of Urtica dioica L.: from preliminary evidence to clinical studies supporting its neuroprotective activity

Urtica dioica is a perennial herb from the family of Urticaceae that is commonly known as stinging nettle. This plant is widespread in Europe, Africa, America, and a part of Asia, as it adapts to different environments and climatic conditions. The leaves, stalk, and bark of U. dioica found applications in the field of nutrition, cosmetics, textile, pest control and pharmacology. In this connection, bioactive chemical constituents such as flavonoids, phenolic acids, amino acids, carotenoids, and fatty acids have been isolated from the plant. With this review, we aim at providing an updated and comprehensive overview of the contributions in literature reporting computational, in vitro, pre-clinical and clinical data supporting the therapeutic applications of U. dioica. Experimental evidence shows that U. dioica constituents and extracts can provide neuroprotective effects by acting through a combination of different molecular mechanisms, that are discussed in the review. These findings could lay the basis for the identification and design of more effective tools against neurodegenerative diseases.

Insights into the promising prospect of medicinal chemistry studies against neurodegenerative disorders

Medicinal chemistry is an interdisciplinary field that incorporates organic chemistry, biochemistry, physical chemistry, pharmacology, informatics, molecular biology, structural biology, cell biology, and other disciplines. Additionally, it considers molecular factors such as the mode of action of the drugs, their chemical structure-activity relationship (SAR), and pharmacokinetic aspects like absorption, distribution, metabolism, elimination, and toxicity. Neurodegenerative disorders (NDs), which are defined by the breakdown of neurons over time, are affecting an increasing number of people. Oxidative stress, particularly the increased production of Reactive Oxygen Species (ROS), plays a crucial role in the growth of various disorders, as indicated by the identification of protein, lipid, and Deoxyribonucleic acid (DNA) oxidation products in vivo. Because of their inherent nature, most biological molecules are vulnerable to ROS, even if they play a role in metabolic parameters and cell signaling. Due to their high polyunsaturated fatty acid content, low antioxidant barrier, and high oxygen uptake, neurons are particularly vulnerable to oxidation by nature. As a result, excessive ROS generation in neurons looks especially harmful, and the mechanisms associated with biomolecule oxidative destruction are several and complex. This review focuses on the formation and management of ROS, as well as their chemical characteristics (both thermodynamic and kinetic), interactions, and implications in NDs.

Computational Chemistry and Molecular Modeling of Reversible MAO Inhibitors

Proper elucidation of drug-target interaction is one of the most significant steps at the early stages of the drug development research. Computer-aided drug design tools have substantial contribution to this stage. In this chapter, we specifically concentrate on the computational methods widely used to develop reversible inhibitors for monoamine oxidase (MAO) isozymes. In this context, current computational techniques in identifying the best drug candidates showing high potency are discussed. The protocols of structure-based drug design methodologies, namely, molecular docking, in silico screening, and molecular dynamics simulations, are presented. Employing case studies of safinamide binding to MAO B, we demonstrate how to use AutoDock 4.2.6 and NAMD software packages.

Discovery of new chemotypes of dual 5-HT2A/D2 receptor antagonists with a strategy of drug design methodologies

Aim: Through the application of structure- and ligand-based methods, the authors aimed to create an integrative approach to developing a computational protocol for the rational drug design of potent dual 5-HT_2A/D₂ receptor antagonists without off-target activities on H₁ receptors. Materials & methods: Molecular dynamics and virtual docking methods were used to identify key interactions of the structurally diverse antagonists in the binding sites of the studied targets, and to generate their bioactive conformations for further 3D-quantitative structure-activity relationship modeling. Results & conclusion: Toward the goal of finding multi-potent drugs with a more effective and safer profile, the obtained results led to the design of a new set of dual antagonists and opened a new perspective on the therapy for complex brain diseases.

Classifying diseases by using biological features to identify potential nosological models

Established nosological models have provided physicians an adequate enough classification of diseases so far. Such systems are important to correctly identify diseases and treat them successfully. However, these taxonomies tend to be based on phenotypical observations, lacking a molecular or biological foundation. Therefore, there is an urgent need to modernize them in order to include the heterogeneous information that is produced in the present, as could be genomic, proteomic, transcriptomic and metabolic data, leading this way to more comprehensive and robust structures. For that purpose, we have developed an extensive methodology to analyse the possibilities when it comes to generate new nosological models from biological features. Different datasets of diseases have been considered, and distinct features related to diseases, namely genes, proteins, metabolic pathways and genetical variants, have been represented as binary and numerical vectors. From those vectors, diseases distances have been computed on the basis of several metrics. Clustering algorithms have been implemented to group diseases, generating different models, each of them corresponding to the distinct combinations of the previous parameters. They have been evaluated by means of intrinsic metrics, proving that some of them are highly suitable to cover new nosologies. One of the clustering configurations has been deeply analysed, demonstrating its quality and validity in the research context, and further biological interpretations have been made. Such model was particularly generated by OPTICS clustering algorithm, by studying the distance between diseases based on gene sharedness and following cosine index metric. 729 clusters were formed in this model, which obtained a Silhouette coefficient of 0.43.

Inhibitory mechanism of an antifungal drug, caspofungin against amyloid β peptide aggregation: Repurposing via neuroinformatics and an experimental approach

The multifactorial neurological condition called Alzheimer's disease (AD) primarily affects elderly individuals. Despite the calamitous consequences of AD, curative strategies for a regimen to apply remain inadequate as several factors contribute to AD etiology. Drug repurposing is an advance strategy prior to drug discovery as various effective drugs perform through alteration of multiple targets, and the present "poly-pharmacology" can be a curative approach to complex disorders. AD's multifactorial behavior actively encourages the hypothesis for a drug design approach focused on drug repurposing. In this study, we discovered that an antifungal drug, Caspofungin (CAS) is a potent Aβ aggregation inhibitor that displays significantly reduced toxicity associated with AD. Drug reprofiling and REMD simulations demonstrated that CAS interacts with the β-sheet section, known as Aβ amyloid fibrils hotspot. CAS leads to destabilization of β-sheet and, conclusively, in its devaluation. Later, in vitro experiments were acquired in which the fibrillar volume was reduced for CAS-treated Aβ peptide. For the first time ever, this study has determined an antifungal agent as the Aβ amyloid aggregation's potent inhibitor. Several efficient sequence-reliant potent inhibitors can be developed in future against the amyloid aggregation for different amyloid peptide by the processing and conformational optimization of CAS.

7-Amine-spiro[chromeno[4,3-b]quinoline-6,1'-cycloalkanes]: Synthesis and cholinesterase inhibitory activity of structurally modified tacrines

Five new examples of 9,10-chloro(bromo)-7-amine-spiro[chromeno[4,3-b]quinoline-6,1'-cycloalkanes] - in which cycloalkanes = cyclopentane, cyclohexane, and cycloheptane - were synthesized at yields of 42-56%, using a sequential one-pot two-step cyclocondensation reaction of three different scaffolds of 2-aminobenzonitriles and the respective spiro[chroman-2,1'-cycloalkan]-4-ones, and using AlCl₃ as the catalyst in a solvent-free method. Subsequently, the five new spirochromeno-quinolines and nine quinolines previously published by us (14 modified tacrine scaffolds) were subjected to AChE and BChE inhibitory activity evaluation. The molecule containing a spirocyclopentane derivative had the highest AChE and BChE inhibitory activity (IC₅₀ = 3.60 and 4.40 μM, respectively), and in general, the non-halogenated compounds were better inhibitors of AChE and BChE than the halogenated molecules. However, the inhibitory potency of compounds 3a-n was weaker than that of tacrine. By molecular docking simulations, it was found that the size of the spirocarbocyclic moieties is inversely proportional to the inhibitory activity of the cholinesterases, probably because an increase in the size of the spirocyclic component sterically hindered the interaction of tacrine derivatives with the active site of tested cholinesterases. The findings obtained here may help in the design and development of new anticholinesterase drugs.

Current computer-aided drug design methodologies in discovery of novel drug candidates for neuropsychiatric and inflammatory diseases

Drug discovery and development is a very challenging, expensive and time-consuming process. Impressive technological advances in computer sciences and molecular biology have made it possible to use computer-aided drug design (CADD) methods in various stages of the drug discovery and development pipeline. Nowadays, CADD presents an efficacious and indispensable tool, widely used in medicinal chemistry, to lead rational drug design and synthesis of novel compounds. In this article, an overview of commonly used CADD approaches from hit identification to lead optimization was presented. Moreover, different aspects of design of multitarget ligands for neuropsychiatric and anti-inflammatory diseases were summarized. Apparently, designing multi-target directed ligands for treatment of various complex diseases may offer better efficacy, and fewer side effects. Antipsychotics that act through aminergic G protein-coupled receptors (GPCRs), especially Dopamine D2 and serotonin 5-HT2A receptors, are the best option for treatment of various symptoms associated with neuropsychiatric disorders. Furthermore, multi-target directed cyclooxygenase-2 (COX-2) and 5-lipoxygenase (5-LOX) inhibitors are also a successful approach to aid the discovery of new anti-inflammatory drugs with fewer side effects. Overall, employing CADD approaches in the process of rational drug design provides a great opportunity for future development, allowing rapid identification of compounds with the optimal polypharmacological profile.

Signature-based approaches for informed drug repurposing: targeting CNS disorders

CNS disorders, and in particular psychiatric illnesses, lack definitive disease-altering therapeutics. The limited understanding of the mechanisms driving these illnesses with the slow pace and high cost of drug development exacerbates this issue. For these reasons, drug repurposing - both a less expensive and time-efficient practice compared to de novo drug development - has been a promising strategy to overcome the paucity of treatments available for these debilitating disorders. While empirical drug-repurposing has been a routine practice in clinical psychiatry, innovative, informed, and cost-effective repurposing efforts using big data ("omics") have been designed to characterize drugs by structural and transcriptomic signatures. These strategies, in conjunction with ontological integration, provide an important opportunity to address knowledge-based challenges associated with drug development for CNS disorders. In this review, we discuss various signature-based in silico approaches to drug repurposing, its integration with multiple omics platforms, and how this data can be used for clinically relevant, evidence-based drug repurposing. These tools provide an exciting translational avenue to merge omics-based drug discovery platforms with patient-specific disease signatures, ultimately facilitating the identification of new therapies for numerous psychiatric disorders.

Newly Synthesized Fluorinated Cinnamylpiperazines Possessing Low In Vitro MAO-B Binding

Herein, we report on the synthesis and pharmacological evaluation of ten novel fluorinated cinnamylpiperazines as potential monoamine oxidase B (MAO-B) ligands. The designed derivatives consist of either cinnamyl or 2-fluorocinnamyl moieties connected to 2-fluoropyridylpiperazines. The three-step synthesis starting from commercially available piperazine afforded the final products in overall yields between 9% and 29%. An in vitro competitive binding assay using l-[³H]Deprenyl as radioligand was developed and the MAO-B binding affinities of the synthesized derivatives were assessed. Docking studies revealed that the compounds 8–17 were stabilized in both MAO-B entrance and substrate cavities, thus resembling the binding pose of l-Deprenyl. Although our results revealed that the novel fluorinated cinnamylpiperazines 8–17 do not possess sufficient MAO-B binding affinity to be eligible as positron emission tomography (PET) agents, the herein developed binding assay and the insights gained within our docking studies will certainly pave the way for further development of MAO-B ligands.

Multi-targeted drug design strategies for the treatment of schizophrenia

INTRODUCTION

Schizophrenia is a complex psychiatric disease (or a conglomeration of disorders) manifesting with positive, negative and cognitive symptoms. The pathophysiology of schizophrenia is not completely known; however, it involves many neurotransmitters and their receptors. In order to treat schizophrenia, drugs need to be multi-target drugs. Indeed, the action of second and third generation antipsychotics involves interactions with many receptors, belonging mainly to aminergic GPCRs.

AREAS COVERED

In this review, the authors summarize current concepts of schizophrenia with the emphasis on the modern dopaminergic, serotoninergic, and glutamatergic hypotheses. Next, they discuss treatments of the disease, stressing multi-target antipsychotics. They cover different aspects of design of multi-target ligands, including the application of molecular modeling approaches for the design and benefits and limitations of multifunctional compounds. Finally, they present successful case studies of multi-target drug design against schizophrenia.

EXPERT OPINION

Treatment of schizophrenia requires the application of multi-target drugs. While designing single target drugs is relatively easy, designing multifunctional compounds is a challenge due to the necessity to balance the affinity to many targets, while avoiding promiscuity and the problems with drug-likeness. Multi-target drugs bring many benefits: better efficiency, fewer adverse effects, and drug-drug interactions and better patient compliance to drug regime.

Future avenues for Alzheimer's disease detection and therapy: liquid biopsy, intracellular signaling modulation, systems pharmacology drug discovery

When Alzheimer's disease (AD) disease-modifying therapies will be available, global healthcare systems will be challenged by a large-scale demand for clinical and biological screening. Validation and qualification of globally accessible, minimally-invasive, and time-, cost-saving blood-based biomarkers need to be advanced. Novel pathophysiological mechanisms (and related candidate biomarkers) - including neuroinflammation pathways (TREM2 and YKL-40), axonal degeneration (neurofilament light chain protein), synaptic dysfunction (neurogranin, synaptotagmin, α-synuclein, and SNAP-25) - may be integrated into an expanding pathophysiological and biomarker matrix and, ultimately, integrated into a comprehensive blood-based liquid biopsy, aligned with the evolving ATN + classification system and the precision medicine paradigm. Liquid biopsy-based diagnostic and therapeutic algorithms are increasingly employed in Oncology disease-modifying therapies and medical practice, showing an enormous potential for AD and other brain diseases as well. For AD and other neurodegenerative diseases, newly identified aberrant molecular pathways have been identified as suitable therapeutic targets and are currently investigated by academia/industry-led R&D programs, including the nerve-growth factor pathway in basal forebrain cholinergic neurons, the sigma1 receptor, and the GTPases of the Rho family. Evidence for a clinical long-term effect on cognitive function and brain health span of cholinergic compounds, drug candidates for repositioning programs, and non-pharmacological multidomain interventions (nutrition, cognitive training, and physical activity) is developing as well. Ultimately, novel pharmacological paradigms, such as quantitative systems pharmacology-based integrative/explorative approaches, are gaining momentum to optimize drug discovery and accomplish effective pathway-based strategies for precision medicine. This article is part of the special issue on 'The Quest for Disease-Modifying Therapies for Neurodegenerative Disorders'.

3D-QSAR and Docking Studies on Pyrimidine Derivatives as CSF-1R Inhibitors

Background:

Colony Stimulating Factor-1 Receptor (CSF-1R) is associated with malignancy, invasiveness and poor prognosis of tumors, and pyrimidine derivatives are considered as a novel class of CSF-1R inhibitor.

Methods:

To explore the relationship between the structures of substituted pyrimidine derivatives and their inhibitory activities against CSF-1R, CoMFA and CoMSIA analyses, and molecular docking studies were performed on a dataset of forty-four compounds.

Results:

We found in CoMFA model including steric and electrostatic fields for the training set, the cross-validated q2 value was 0.617 and the non-cross-validated r2 value was 0.983. While, the crossvalidated q2 value was 0.637 and the non-cross-validated r2 value was 0.984 in CoMSIA Model which include steric, electrostatic and hydrophobic fields. 3D equipotential maps generated from CoMFA and CoMSIA along with the docking binding structures provided enough information about the structural requirements for better activity.

Conclusion:

The data generated from the present study helped us to predict the activity of new inhibitors and further design some novel and potent CSF-1R inhibitors.

Natural Compounds Therapeutic Features in Brain Disorders by Experimental, Bioinformatics and Cheminformatics Methods

BACKGROUND

Synthetic compounds with pharmaceutical applications in brain disorders are daily designed and synthesized, with well first effects but also seldom severe side effects. This imposes the search for alternative therapies based on the pharmaceutical potentials of natural compounds. The natural compounds isolated from various plants and arthropods venom are well known for their antimicrobial (antibacterial, antiviral) and antiinflammatory activities, but more studies are needed for a better understanding of their structural and pharmacological features with new therapeutic applications.

OBJECTIVES

Here we present some structural and pharmaceutical features of natural compounds isolated from plants and arthropods venom relevant for their efficiency and potency in brain disorders. We present the polytherapeutic effects of natural compounds belonging to terpenes (limonene), monoterpenoids (1,8-cineole) and stilbenes (resveratrol), as well as natural peptides (apamin, mastoparan and melittin).

METHODS

Various experimental and in silico methods are presented with special attention on bioinformatics (natural compounds database, artificial neural network) and cheminformatics (QSAR, drug design, computational mutagenesis, molecular docking).

RESULTS

In the present paper we reviewed: (i) recent studies regarding the pharmacological potential of natural compounds in the brain; (ii) the most useful databases containing molecular and functional features of natural compounds; and (iii) the most important molecular descriptors of natural compounds in comparison with a few synthetic compounds.

CONCLUSION

Our paper indicates that natural compounds are a real alternative for nervous system therapy and represents a helpful tool for the future papers focused on the study of the natural compounds.

Neuroprotective properties of RT10, a fraction isolated from Parawixia bistriata spider venom, against excitotoxicity injury in neuron-glia cultures

BACKGROUND

L-Glutamate (L-Glu), the major excitatory neurotransmitter in the mammalian Central Nervous System (CNS), is essential to cognitive functions. However, when L-Glu is accumulated in large concentrations at the synaptic cleft, it can induce excitotoxicity that results in secondary damage implicated in many neurological disorders. Current therapies for the treatment of neurological disorders are ineffective and have side effects associated with their use; therefore, there is a need to develop novel treatments. In this regard, previous studies have shown that neuroactive compounds obtained from the venom of the spider Parawixia bistriata have neuroprotective effects in vitro and in vivo. In this sense, this work aimed to evaluate potential neuroprotective effects of fraction RT10, obtained from this spider venom, on primary cultures of neuron and glial cells subjected to glutamate excitotoxicity insults.

METHODS

Primary cultures of neurons and glia were obtained from the cerebral tissue of 1-day-old postnatal Wistar rats. After 7 days in vitro (DIV), the cultures were incubated with fraction RT10 (0.002; 0.02; 0.2 and 2 µg/µL) or riluzole (100 µM) for 3-hours before application of 5 mM L-Glu. After 12 hours, the resazurin sodium salt (RSS) test was applied to measure metabolic activity and proliferation of living cells, whereas immunocytochemistry for MAP2 was performed to measure neuronal survival. In addition, the cells were immunolabeled with NeuN and GFAP in baseline conditions.

RESULTS

In the RSS tests, we observed that pre-incubation with RT10 before the excitotoxic insults from L-Glu resulted in neuroprotection, shown by a 10% reduction in the cell death level. RT10 was more effective than riluzole, which resulted in a cell-death reduction of 5%. Moreover, qualitative analysis of neuronal morphology (by MAP2 staining, expressed as fluorescence intensity (FI), an indirect measure of neuronal survival) indicate that RT10 reduced the toxic effects of L-Glu, as shown by a 38 % increase in MAP2 fluorescence when compared to L-Glu insult. On the other hand, the riluzole treatment resulted in 17% increase of MAP2 fluorescence; therefore, the neuroprotection from RT10 was more efficacious.

CONCLUSION

RT10 fraction exhibits neuroprotective effects against L-Glu excitotoxicity in neuron-glia cultured in vitro.

Benzoic acid-derived nitrones: A new class of potential acetylcholinesterase inhibitors and neuroprotective agents

The discovery of new chemical entities endowed with potent and selective acetylcholinesterase (AChE) and/or butyrylcholinesterase (BChE) inhibitory activity is still a relevant subject for Alzheimer's disease therapy. Therefore, a small library of benzoic based amide nitrones (compounds 24 to 42) was synthesized and screened toward cholinesterase enzymes. SAR studies showed that the tert-butyl moiety is the most favourable nitrone pattern. In general, tert-butyl derivatives effectively inhibited AChE, being compound 33 the most potent (IC₅₀ = 8.3 ± 0.3 μM; Ki 5.2 μM). The data pointed to a non-competitive inhibition mechanism of action, which was also observed for the standard donepezil. None of compounds showed BChE inhibitory activity. Molecular modelling studies provided insights into the enzyme-inhibitor interactions and rationalised the experimental data, confirming that the binding mode of nitrones 33 and 38 towards AChE has the most favourable binding free energy. The tert-butylnitrones 33 and 38 were not cytotoxic on different cell lines (SH-SY5Y and HepG2). Moreover, compound 33 was able to prevent t-BHP-induced oxidative stress in SH-SY5Y differentiated cells. Due to its AChE selectivity and promising cytoprotective properties, as well as its appropriate drug-like profile pointing toward blood-brain barrier permeability, compound 33 is proposed as a valid lead for a further optimization step.

Integration of target discovery, drug discovery and drug delivery: A review on computational strategies

Most of the computational tools involved in drug discovery developed during the 1980s were largely based on computational chemistry, quantitative structure-activity relationship (QSAR) and cheminformatics. Subsequently, the advent of genomics in the 2000s gave rise to a huge number of databases and computational tools developed to analyze large quantities of data, through bioinformatics, to obtain valuable information about the genomic regulation of different organisms. Target identification and validation is a long process during which evidence for and against a target is accumulated in the pursuit of developing new drugs. Finally, the drug delivery system appears as a novel approach to improve drug targeting and releasing into the cells, leading to new opportunities to improve drug efficiency and avoid potential secondary effects. In each area: target discovery, drug discovery and drug delivery, different computational strategies are being developed to accelerate the process of selection and discovery of new tools to be applied to different scientific fields. Research on these three topics is growing rapidly, but still requires a global view of this landscape to detect the most challenging bottleneck and how computational tools could be integrated in each topic. This review describes the current state of the art in computational strategies for target discovery, drug discovery and drug delivery and how these fields could be integrated. Finally, we will discuss about the current needs in these fields and how the continuous development of databases and computational tools will impact on the improvement of those areas. This article is categorized under: Therapeutic Approaches and Drug Discovery > Emerging Technologies Therapeutic Approaches and Drug Discovery > Nanomedicine for Infectious Disease Nanotechnology Approaches to Biology > Nanoscale Systems in Biology.

Novel Approach for the Search for Chemical Scaffolds with Dual Activity with Acetylcholinesterase and the α7 Nicotinic Acetylcholine Receptor-A Perspective for the Treatment of Neurodegenerative Disorders

Neurodegenerative disorders, including Alzheimer’s disease, belong to the group of the most difficult and challenging conditions with very limited treatment options. Attempts to find new drugs in most cases fail at the clinical stage. New tactics to develop better drug candidates to manage these diseases are urgently needed. It is evident that better understanding of the neurodegeneration process is required and targeting multiple receptors may be essential. Herein, we present a novel approach, searching for dual active compounds interacting with acetylcholinesterase (AChE) and the α7 nicotinic acetylcholine receptor (nAChR) using computational chemistry methods including homology modelling and high throughput virtual screening. Activities of identified hits were evaluated at the two targets using the colorimetric method of Ellman and two-electrode voltage-clamp electrophysiology, respectively. Out of 87,250 compounds from a ZINC database of natural products and their derivatives, we identified two compounds, 8 and 9, with dual activity and balanced IC₅₀ values of 10 and 5 µM at AChE, and 34 and 14 µM at α7 nAChR, respectively. This is the first report presenting successful use of virtual screening in finding compounds with dual mode of action inhibiting both the AChE enzyme and the α7 nAChR and shows that computational methods can be a valuable tool in the early lead discovery process.

Multi-Target Approach for Drug Discovery against Schizophrenia

Polypharmacology is nowadays considered an increasingly crucial aspect in discovering new drugs as a number of original single-target drugs have been performing far behind expectations during the last ten years. In this scenario, multi-target drugs are a promising approach against polygenic diseases with complex pathomechanisms such as schizophrenia. Indeed, second generation or atypical antipsychotics target a number of aminergic G protein-coupled receptors (GPCRs) simultaneously. Novel strategies in drug design and discovery against schizophrenia focus on targets beyond the dopaminergic hypothesis of the disease and even beyond the monoamine GPCRs. In particular these approaches concern proteins involved in glutamatergic and cholinergic neurotransmission, challenging the concept of antipsychotic activity without dopamine D₂ receptor involvement. Potentially interesting compounds include ligands interacting with glycine modulatory binding pocket on N-methyl-d-aspartate (NMDA) receptors, positive allosteric modulators of α-Amino-3-hydroxy-5-methyl-4-isoxazolepropionic acid (AMPA) receptors, positive allosteric modulators of metabotropic glutamatergic receptors, agonists and positive allosteric modulators of α7 nicotinic receptors, as well as muscarinic receptor agonists. In this review we discuss classical and novel drug targets for schizophrenia, cover benefits and limitations of current strategies to design multi-target drugs and show examples of multi-target ligands as antipsychotics, including marketed drugs, substances in clinical trials, and other investigational compounds.

Combining in vitro and in silico Approaches to Find New Candidate Drugs Targeting the Pathological Proteins Related to the Alzheimer's Disease

Background: Alzheimer’s disease (AD) as the most common cause of dementia among older people has aroused the universal concern of the whole world. However, until now there is still none effective treatments. Consequently, the development of new drugs targeting this complicated brain disorder is urgent and needs more efforts. In this review, we detailed the current state of knowledge about new candidate drugs targeting the pathological proteins especially the drugs which are employed using the combined methods of in vitro and in silico.

Methods: We looked up and reviewed online papers related to the pathogenesis and new drugs development of AD. Then, articles up to the requirements were respectively analyzed and summaried to provide the latest knowledge about the pathogenic effect and the new candidate drugs targeting Aβ and Tau proteins.

Results: New candidate drugs targeting the Aβ include decreasing the production, promoting the clearence and preventing aggregation. However these drugs have mostly failed in Phase III clinical trial stage due to the unsuccessful of reversing cognition symptoms. As to tau protein, the prevention of tau aggregation and propagation is a promising strategy to synthesize/design mechanism-based drugs against tauopathies. Some candidate drugs are under research. Moreover, because of the complex pathogenesis of AD, multi-target drugs have also shed light on the treatment of AD.

Conclusion: Given to the consecutive failure of Aβ-directed drugs and the feasibilities of tau-targeted therapy, more and more researchers suggested that the AD treatment should be moved from Aβ to tau or focused on considering the soluble form of Aβ and tau as a whole. Moreover, the novel in silico methods also have great potential in drug discovery, drug repositioning, virtual screening of chemical libraries. No matter how many difficulties and challenges in prevention and treatment of AD, we firmly believe that the effective and safe drugs will be found using the combined methods in the immediate future with the global effort.

Blood Brain Barrier and Alzheimer's Disease: Similarity and Dissimilarity of Molecular Alerts

Background

Blood brain barrier and Alzheimer’s disease are interrelated. This interrelation is detected by physicochemical methods, pharmacological and electrophysiological analyses. Nature of the phenomenon is extremely complex. The description of this interrelation in mathematical terms is a very important task.

Objective

The systematization of facts, which are described in the literature and related to interaction between processes, which influence Alzheimer's disease and blood brain barrier is the subject of this work. In addition, establishing of correlations between molecular features and endpoints, which are related to the treatment of Alzheimer's disease and blood brain barrier using the CORAL software are subjects of this work.

Methods

The information on logically structured analysis is available in the literature and building up quantitative structure – activity relationships (QSARs) by the Monte Carlo method has been used to solve the task of systematization of facts related to the “treatment of Alzheimer's disease vs. blood brain barrier”.

Results

Comparison of agreements and disagreements of the available published papers together with the statistical quality of built up QSARs are results of this work.

Conclusion

The facts from published papers and technical details of QSAR built up in this study give possibility to formulate the following rules: (i) there are molecular alerts, which are promoters to increase blood brain barrier and therapeutic activity of anti-Alzheimer disease agents; (ii) there are molecular alerts, which contradict each other.

Bis(9)-(-)-Meptazinol, a novel dual-binding AChE inhibitor, rescues cognitive deficits and pathological changes in APP/PS1 transgenic mice

BACKGROUND

Alzheimer's disease (AD) is a progressive and irreversible neurodegenerative brain disorder, which is the most common form of dementia. Intensive efforts have been made to find effective and safe treatment against AD. Acetylcholinesterase inhibitors (AChEIs) have been widely used for the treatment of mild to moderate AD. In this study, we investigated the effect of Bis(9)-(-)-Meptazinol (B9M), a novel potential dual-binding acetylcholinesterase (AChE) inhibitor, on learning and memory abilities, as well as the underlying mechanism in the APP/PS1 mouse model of AD.

METHODS

B9M (0.1 μg/kg, 0.3 μg/kg, and 1 μg/kg) was administered by subcutaneous injection into eight-month-old APP/PS1 transgenic mice for four weeks. Morris water maze, nest-building and novel object recognition were used to examine learning and memory ability. Aβ levels and Aβ plaque were evaluated by ELISA and immunochemistry.

RESULTS

Our results showed that chronic treatment with B9M significantly improved the cognitive function of APP/PS1 transgenic mice in the Morris water maze test, nest-building test and novel object recognition test. Moreover, B9M improved cognitive deficits in APP/PS1 mice by a mechanism that may be associated with its inhibition of the AChE activity, Aβ plaque burden, levels of Aβ and the consequent activation of astrocytes and microglia in the brain of APP/PS1 transgenic mice. Most of important, the most effective dose of B9M in the present study is 1 μg/kg, which is one thousand of the dosage of Donepezil acted as the control treatment. Furthermore, B9M reduced Aβ plaque burden better than Donepezil.

CONCLUSION

These results indicate that B9M appears to have potential as an effective AChE inhibitor for the treatment of AD with symptom-relieving and disease-modifying properties.

Identification of Natural Compounds against Neurodegenerative Diseases Using In Silico Techniques

The aim of this study was to identify new potentially active compounds for three protein targets, tropomyosin receptor kinase A (TrkA), N-methyl-d-aspartate (NMDA) receptor, and leucine-rich repeat kinase 2 (LRRK2), that are related to various neurodegenerative diseases such as Alzheimer's, Parkinson's, and neuropathic pain. We used a combination of machine learning methods including artificial neural networks and advanced multilinear techniques to develop quantitative structure⁻activity relationship (QSAR) models for all target proteins. The models were applied to screen more than 13,000 natural compounds from a public database to identify active molecules. The best candidate compounds were further confirmed by docking analysis and molecular dynamics simulations using the crystal structures of the proteins. Several compounds with novel scaffolds were predicted that could be used as the basis for development of novel drug inhibitors related to each target.

Computer-Aided Multi-Target Management of Emergent Alzheimer's Disease

Alzheimer's disease (AD) represents an enormous global health burden in terms of human suffering and economic cost. AD management requires a shift from the prevailing paradigm targeting pathogenesis to design and develop effective drugs with adequate success in clinical trials. Therefore, it is of interest to report a review on amyloid beta (Aβ) effects and other multi-targets including cholinesterase, NFTs, tau protein and TNF associated with brain cell death to be neuro-protective from AD. It should be noted that these molecules have been generated either by target-based or phenotypic methods. Hence, the use of recent advancements in nanomedicine and other natural compounds screening tools as a feasible alternative for circumventing specific liabilities is realized. We review recent developments in the design and identification of neuro-degenerative compounds against AD generated using current advancements in computational multi-target modeling algorithms reflected by theragnosis (combination of diagnostic tests and therapy) concern.

Hydroxybenzoic Acid Derivatives as Dual-Target Ligands: Mitochondriotropic Antioxidants and Cholinesterase Inhibitors

Alzheimer's disease (AD) is a multifactorial age-related disease associated with oxidative stress (OS) and impaired cholinergic transmission. Accordingly, targeting mitochondrial OS and restoring cholinergic transmission can be an effective therapeutic strategy toward AD. Herein, we report for the first time dual-target hydroxybenzoic acid (HBAc) derivatives acting as mitochondriotropic antioxidants and cholinesterase (ChE) inhibitors. The studies were performed with two mitochondriotropic antioxidants AntiOxBEN₁ (catechol derivative), and AntiOxBEN₂ (pyrogallol derivative) and compounds 15–18, which have longer spacers. Compounds AntiOxBEN₁ and 15, with a shorter carbon chain spacer (six- and eight-carbon) were shown to be potent antioxidants and BChE inhibitors (IC₅₀ = 85 ± 5 and 106 ± 5 nM, respectively), while compounds 17 and 18 with a 10-carbon chain were more effective AChE inhibitors (IC₅₀ = 7.7 ± 0.4 and 7.2 ± 0.5 μM, respectively). Interestingly, molecular modeling data pointed toward bifunctional ChEs inhibitors. The most promising ChE inhibitors acted by a non-competitive mechanism. In general, with exception of compounds 15 and 17, no cytotoxic effects were observed in differentiated human neuroblastoma (SH-SY5Y) and human hepatocarcinoma (HepG2) cells, while Aβ-induced cytotoxicity was significantly prevented by the new dual-target HBAc derivatives. Overall, due to its BChE selectivity, favorable toxicological profile, neuroprotective activity and drug-like properties, which suggested blood-brain barrier (BBB) permeability, the mitochondriotropic antioxidant AntiOxBEN₁ is considered a valid lead candidate for the development of dual acting drugs for AD and other mitochondrial OS-related diseases.

MetStabOn-Online Platform for Metabolic Stability Predictions

Metabolic stability is an important parameter to be optimized during the complex process of designing new active compounds. Tuning this parameter with the simultaneous maintenance of a desired compound's activity is not an easy task due to the extreme complexity of metabolic pathways in living organisms. In this study, the platform for in silico qualitative evaluation of metabolic stability, expressed as half-lifetime and clearance was developed. The platform is based on the application of machine learning methods and separate models for human, rat and mouse data were constructed. The compounds' evaluation is qualitative and two types of experiments can be performed-regression, which is when the compound is assigned to one of the metabolic stability classes (low, medium, high) on the basis of numerical value of the predicted half-lifetime, and classification, in which the molecule is directly assessed as low, medium or high stability. The results show that the models have good predictive power, with accuracy values over 0.7 for all cases, for Sequential Minimal Optimization (SMO), k-nearest neighbor (IBk) and Random Forest algorithms. Additionally, for each of the analyzed compounds, 10 of the most similar structures from the training set (in terms of Tanimoto metric similarity) are identified and made available for download as separate files for more detailed manual inspection. The predictive power of the models was confronted with the external dataset, containing metabolic stability assessment via the GUSAR software, leading to good consistency of results for SMOreg and Naïve Bayes (~0.8 on average). The tool is available online.

A perspective on multi-target drug discovery and design for complex diseases

Diseases of infection, of neurodegeneration (such as Alzheimer’s and Parkinson’s diseases), and of malignancy (cancers) have complex and varied causative factors. Modern drug discovery has the power to identify potential modulators for multiple targets from millions of compounds. Computational approaches allow the determination of the association of each compound with its target before chemical synthesis and biological testing is done. These approaches depend on the prior identification of clinically and biologically validated targets. This Perspective will focus on the molecular and computational approaches that underpin drug design by medicinal chemists to promote understanding and collaboration with clinical scientists.

Revolution of Alzheimer Precision Neurology. Passageway of Systems Biology and Neurophysiology

The Precision Neurology development process implements systems theory with system biology and neurophysiology in a parallel, bidirectional research path: a combined hypothesis-driven investigation of systems dysfunction within distinct molecular, cellular, and large-scale neural network systems in both animal models as well as through tests for the usefulness of these candidate dynamic systems biomarkers in different diseases and subgroups at different stages of pathophysiological progression. This translational research path is paralleled by an "omics"-based, hypothesis-free, exploratory research pathway, which will collect multimodal data from progressing asymptomatic, preclinical, and clinical neurodegenerative disease (ND) populations, within the wide continuous biological and clinical spectrum of ND, applying high-throughput and high-content technologies combined with powerful computational and statistical modeling tools, aimed at identifying novel dysfunctional systems and predictive marker signatures associated with ND. The goals are to identify common biological denominators or differentiating classifiers across the continuum of ND during detectable stages of pathophysiological progression, characterize systems-based intermediate endophenotypes, validate multi-modal novel diagnostic systems biomarkers, and advance clinical intervention trial designs by utilizing systems-based intermediate endophenotypes and candidate surrogate markers. Achieving these goals is key to the ultimate development of early and effective individualized treatment of ND, such as Alzheimer's disease. The Alzheimer Precision Medicine Initiative (APMI) and cohort program (APMI-CP), as well as the Paris based core of the Sorbonne University Clinical Research Group "Alzheimer Precision Medicine" (GRC-APM) were recently launched to facilitate the passageway from conventional clinical diagnostic and drug development toward breakthrough innovation based on the investigation of the comprehensive biological nature of aging individuals. The APMI movement is gaining momentum to systematically apply both systems neurophysiology and systems biology in exploratory translational neuroscience research on ND.

Computer-aided design of multi-target ligands at A1R, A2AR and PDE10A, key proteins in neurodegenerative diseases

Compounds designed to display polypharmacology may have utility in treating complex diseases, where activity at multiple targets is required to produce a clinical effect. In particular, suitable compounds may be useful in treating neurodegenerative diseases by promoting neuronal survival in a synergistic manner via their multi-target activity at the adenosine A1 and A2A receptors (A1R and A2AR) and phosphodiesterase 10A (PDE10A), which modulate intracellular cAMP levels. Hence, in this work we describe a computational method for the design of synthetically feasible ligands that bind to A1 and A2A receptors and inhibit phosphodiesterase 10A (PDE10A), involving a retrosynthetic approach employing in silico target prediction and docking, which may be generally applicable to multi-target compound design at several target classes. This approach has identified 2-aminopyridine-3-carbonitriles as the first multi-target ligands at A1R, A2AR and PDE10A, by showing agreement between the ligand and structure based predictions at these targets. The series were synthesized via an efficient one-pot scheme and validated pharmacologically as A1R/A2AR-PDE10A ligands, with IC50 values of 2.4-10.0 μM at PDE10A and Ki values of 34-294 nM at A1R and/or A2AR. Furthermore, selectivity profiling of the synthesized 2-amino-pyridin-3-carbonitriles against other subtypes of both protein families showed that the multi-target ligand 8 exhibited a minimum of twofold selectivity over all tested off-targets. In addition, both compounds 8 and 16 exhibited the desired multi-target profile, which could be considered for further functional efficacy assessment, analog modification for the improvement of selectivity towards A1R, A2AR and PDE10A collectively, and evaluation of their potential synergy in modulating cAMP levels.

Polypharmacology of conformationally locked methanocarba nucleosides

A single molecular scaffold can be adapted to interact with diverse targets, either separately or simultaneously. Nucleosides and nucleotides in which ribose is substituted with bicyclo[3.1.0]hexane are an example of a versatile drug-like scaffold for increasing selectivity at their classical targets: kinases, polymerases, adenosine and P2 receptors. Also, by applying structure-based functional group manipulations, rigidified adenosine derivatives can be repurposed to satisfy pharmacophoric requirements of various GPCRs, ion channels, enzymes and transporters, initially detected as off-target activities. Recent examples include 5HT2B serotonin receptor antagonists and novel dopamine transporter allosteric modulators. This directable target diversity establishes rigid nucleosides as privileged scaffolds.

Tailored Approaches in Drug Development and Diagnostics: From Molecular Design to Biological Model Systems

Approaches to increase the efficiency in developing drugs and diagnostics tools, including new drug delivery and diagnostic technologies, are needed for improved diagnosis and treatment of major diseases and health problems such as cancer, inflammatory diseases, chronic wounds, and antibiotic resistance. Development within several areas of research ranging from computational sciences, material sciences, bioengineering to biomedical sciences and bioimaging is needed to realize innovative drug development and diagnostic (DDD) approaches. Here, an overview of recent progresses within key areas that can provide customizable solutions to improve processes and the approaches taken within DDD is provided. Due to the broadness of the area, unfortunately all relevant aspects such as pharmacokinetics of bioactive molecules and delivery systems cannot be covered. Tailored approaches within (i) bioinformatics and computer-aided drug design, (ii) nanotechnology, (iii) novel materials and technologies for drug delivery and diagnostic systems, and (iv) disease models to predict safety and efficacy of medicines under development are focused on. Current developments and challenges ahead are discussed. The broad scope reflects the multidisciplinary nature of the field of DDD and aims to highlight the convergence of biological, pharmaceutical, and medical disciplines needed to meet the societal challenges of the 21st century.

The Forty-Sixth Euro Congress on Drug Synthesis and Analysis: Snapshot †

The 46th EuroCongress on Drug Synthesis and Analysis (ECDSA-2017) was arranged within the celebration of the 65th Anniversary of the Faculty of Pharmacy at Comenius University in Bratislava, Slovakia from 5-8 September 2017 to get together specialists in medicinal chemistry, organic synthesis, pharmaceutical analysis, screening of bioactive compounds, pharmacology and drug formulations; promote the exchange of scientific results, methods and ideas; and encourage cooperation between researchers from all over the world. The topic of the conference, "Drug Synthesis and Analysis," meant that the symposium welcomed all pharmacists and/or researchers (chemists, analysts, biologists) and students interested in scientific work dealing with investigations of biologically active compounds as potential drugs. The authors of this manuscript were plenary speakers and other participants of the symposium and members of their research teams. The following summary highlights the major points/topics of the meeting.

In silico repurposing of antipsychotic drugs for Alzheimer's disease

Background

Alzheimer’s disease (AD) is the most prevalent form of dementia and represents one of the highest unmet requirements in medicine today. There is shortage of novel molecules entering into market because of poor pharmacokinetic properties and safety issues. Drug repurposing offers an opportunity to reinvigorate the slowing drug discovery process by finding new uses for existing drugs. The major advantage of the drug repurposing approach is that the safety issues are already investigated in the clinical trials and the drugs are commercially available in the marketplace. As this approach provides an effective solution to hasten the process of providing new alternative drugs for AD, the current study shows the molecular interaction of already known antipsychotic drugs with the different protein targets implicated in AD using in silico studies.

Result

A computational method based on ligand–protein interaction was adopted in present study to explore potential antipsychotic drugs for the treatment of AD. The screening of approximately 150 antipsychotic drugs was performed on five major protein targets (AChE, BuChE, BACE 1, MAO and NMDA) by molecular docking. In this study, for each protein target, the best drug was identified on the basis of dock score and glide energy. The top hits were then compared with the already known inhibitor of the respective proteins. Some of the drugs showed relatively better docking score and binding energies as compared to the already known inhibitors of the respective targets. Molecular descriptors like molecular weight, number of hydrogen bond donors, acceptors, predicted octanol/water partition coefficient and percentage human oral absorption were also analysed to determine the in silico ADME properties of these drugs and all were found in the acceptable range and follows Lipinski’s rule.

Conclusion

The present study have led to unravel the potential of leading antipsychotic drugs such as pimozide, bromperidol, melperone, anisoperidone, benperidol and anisopirol against multiple targets associated with AD. Benperidol was found to be the best candidate drug interacting with different target proteins involved in AD.

Assessment of Enzyme Inhibition: A Review with Examples from the Development of Monoamine Oxidase and Cholinesterase Inhibitory Drugs

The actions of many drugs involve enzyme inhibition. This is exemplified by the inhibitors of monoamine oxidases (MAO) and the cholinsterases (ChE) that have been used for several pharmacological purposes. This review describes key principles and approaches for the reliable determination of enzyme activities and inhibition as well as some of the methods that are in current use for such studies with these two enzymes. Their applicability and potential pitfalls arising from their inappropriate use are discussed. Since inhibitor potency is frequently assessed in terms of the quantity necessary to give 50% inhibition (the IC50 value), the relationships between this and the mode of inhibition is also considered, in terms of the misleading information that it may provide. Incorporation of more than one functionality into the same molecule to give a multi-target-directed ligands (MTDLs) requires careful assessment to ensure that the specific target effects are not significantly altered and that the kinetic behavior remains as favourable with the MTDL as it does with the individual components. Such factors will be considered in terms of recently developed MTDLs that combine MAO and ChE inhibitory functions.

Antinociceptive Activity of Borreria verticillata: In vivo and In silico Studies

Borreria verticillata (L.) G. Mey. known vassourinha has antibacterial, antimalarial, hepatoprotective, antioxidative, analgesic, and anti-inflammatory, however, its antinociceptive action requires further studies. Aim of the study evaluated the antinociceptive activity of B. verticillata hydroalcoholic extract (EHBv) and ethyl acetate fraction (FAc) by in vivo and in silico studies. In vivo assessment included the paw edema test, writhing test, formalin test and tail flick test. Wistar rats and Swiss mice were divided into 6 groups and given the following treatments oral: 0.9% NaCl control group (CTRL), 10 mg/kg memantine (MEM), 10 mg/kg indomethacin (INDO), 500 mg/kg EHBv (EHBv 500), 25 mg/kg FAc (FAc 25) and 50 mg/kg FAc (FAc 50). EHBv, FAc 25 and 50 treatments exhibited anti-edematous and peripheral antinociceptive effects. For in silico assessment, compounds identified in FAc were subjected to molecular docking with COX-2, GluN1a and GluN2B. Ursolic acid (UA) was the compound with best affinity parameters (binding energy and inhibition constant) for COX-2, GluN1a, GluN2B, and was selected for further analysis with molecular dynamics (MD) simulations. In MD simulations, UA exhibited highly frequent interactions with residues Arg120 and Glu524 in the COX-2 active site and NMDA, whereby it might prevent COX-2 and NMDA receptor activation. Treatment with UA 10 mg/Kg showed peripheral and central antinociceptive effect. The antinociceptive effect of B. verticillata might be predominantly attributed to peripheral actions, including the participation of anti-inflammatory components. Ursolic acid is the main active component and seems to be a promising source of COX-2 inhibitors and NMDA receptor antagonists.

Monoaminergic Mechanisms in Epilepsy May Offer Innovative Therapeutic Opportunity for Monoaminergic Multi-Target Drugs

A large body of experimental and clinical evidence has strongly suggested that monoamines play an important role in regulating epileptogenesis, seizure susceptibility, convulsions, and comorbid psychiatric disorders commonly seen in people with epilepsy (PWE). However, neither the relative significance of individual monoamines nor their interaction has yet been fully clarified due to the complexity of these neurotransmitter systems. In addition, epilepsy is diverse, with many different seizure types and epilepsy syndromes, and the role played by monoamines may vary from one condition to another. In this review, we will focus on the role of serotonin, dopamine, noradrenaline, histamine, and melatonin in epilepsy. Recent experimental, clinical, and genetic evidence will be reviewed in consideration of the mutual relationship of monoamines with the other putative neurotransmitters. The complexity of epileptic pathogenesis may explain why the currently available drugs, developed according to the classic drug discovery paradigm of "one-molecule-one-target," have turned out to be effective only in a percentage of PWE. Although, no antiepileptic drugs currently target specifically monoaminergic systems, multi-target directed ligands acting on different monoaminergic proteins, present on both neurons and glia cells, may represent a new approach in the management of seizures, and their generation as well as comorbid neuropsychiatric disorders.