Inhibition of GCK-IV kinases dissociates cell death and axon regeneration in CNS neurons.

Axon injury is a hallmark of many neurodegenerative diseases, often resulting in neuronal cell death and functional impairment. Dual leucine zipper kinase (DLK) has emerged as a key mediator of this process. However, while DLK inhibition is robustly protective in a wide range of neurodegenerative disease models, it also inhibits axonal regeneration. Indeed, there are no genetic perturbations that are known to both improve long-term survival and promote regeneration. To identify such a neuroprotective target, we conducted a set of complementary high-throughput screens using a protein kinase inhibitor library in human stem cell-derived retinal ganglion cells (hRGCs). Overlapping compounds that promoted both neuroprotection and neurite outgrowth were bioinformatically deconvoluted to identify specific kinases that regulated neuronal death and axon regeneration. This work identified the role of germinal cell kinase four (GCK-IV) kinases in cell death and additionally revealed their unexpected activity in suppressing axon regeneration. Using an adeno-associated virus (AAV) approach, coupled with genome editing, we validated that GCK-IV kinase knockout improves neuronal survival, comparable to that of DLK knockout, while simultaneously promoting axon regeneration. Finally, we also found that GCK-IV kinase inhibition also prevented the attrition of RGCs in developing retinal organoid cultures without compromising axon outgrowth, addressing a major issue in the field of stem cell-derived retinas. Together, these results demonstrate a role for the GCK-IV kinases in dissociating the cell death and axonal outgrowth in neurons and their druggability provides for therapeutic options for neurodegenerative diseases.

The Kinase Chemogenomic Set (KCGS): An Open Science Resource for Kinase Vulnerability Identification.

We describe the assembly and annotation of a chemogenomic set of protein kinase inhibitors as an open science resource for studying kinase biology. The set only includes inhibitors that show potent kinase inhibition and a narrow spectrum of activity when screened across a large panel of kinase biochemical assays. Currently, the set contains 187 inhibitors that cover 215 human kinases. The kinase chemogenomic set (KCGS), current Version 1.0, is the most highly annotated set of selective kinase inhibitors available to researchers for use in cell-based screens.

Detection of drug-drug interactions through data mining studies using clinical sources, scientific literature and social media.

Drug-drug interactions (DDIs) constitute an important concern in drug development and postmarketing pharmacovigilance. They are considered the cause of many adverse drug effects exposing patients to higher risks and increasing public health system costs. Methods to follow-up and discover possible DDIs causing harm to the population are a primary aim of drug safety researchers. Here, we review different methodologies and recent advances using data mining to detect DDIs with impact on patients. We focus on data mining of different pharmacovigilance sources, such as the US Food and Drug Administration Adverse Event Reporting System and electronic health records from medical institutions, as well as on the diverse data mining studies that use narrative text available in the scientific biomedical literature and social media. We pay attention to the strengths but also further explain challenges related to these methods. Data mining has important applications in the analysis of DDIs showing the impact of the interactions as a cause of adverse effects, extracting interactions to create knowledge data sets and gold standards and in the discovery of novel and dangerous DDIs.

Exploring the Multi-Target Performance of Mitochondriotropic Antioxidants against the Pivotal Alzheimer's Disease Pathophysiological Hallmarks.

Alzheimer disease (AD) is the most common neurodegenerative disease featuring progressive and degenerative neurological impairments resulting in memory loss and cognitive decline. The specific mechanisms underlying AD are still poorly understood, but it is suggested that a deficiency in the brain neurotransmitter acetylcholine, the deposition of insoluble aggregates of fibrillar ß-amyloid 1-42 (Aß42), and iron and glutamate accumulation play an important role in the disease progress. Despite the existence of approved cholinergic drugs, none of them demonstrated effectiveness in modifying disease progression. Accordingly, the development of new chemical entities acting on more than one target is attracting progressively more attention as they can tackle intricate network targets and modulate their effects. Within this endeavor, a series of mitochondriotropic antioxidants inspired on hydroxycinnamic (HCA's) scaffold were synthesized, screened toward cholinesterases and evaluated as neuroprotectors in a differentiated human SH-SY5Y cell line. From the series, compounds 7 and 11 with a 10-carbon chain can be viewed as multi-target leads for the treatment of AD, as they act as dual and bifunctional cholinesterase inhibitors and prevent the neuronal damage caused by diverse aggressors related to protein misfolding and aggregation, iron accumulation and excitotoxicity.

Adenosine Receptor Ligands: Coumarin-Chalcone Hybrids as Modulating Agents on the Activity of hARs.

Adenosine receptors (ARs) play an important role in neurological and psychiatric disorders such as Alzheimer's disease, Parkinson's disease, epilepsy and schizophrenia. The different subtypes of ARs and the knowledge on their densities and status are important for understanding the mechanisms underlying the pathogenesis of diseases and for developing new therapeutics. Looking for new scaffolds for selective AR ligands, coumarin-chalcone hybrids were synthesized (compounds 1-8) and screened in radioligand binding (hA1, hA2A and hA3) and adenylyl cyclase (hA2B) assays in order to evaluate their affinity for the four human AR subtypes (hARs). Coumarin-chalcone hybrid has been established as a new scaffold suitable for the development of potent and selective ligands for hA1 or hA3 subtypes. In general, hydroxy-substituted hybrids showed some affinity for the hA1, while the methoxy counterparts were selective for the hA3. The most potent hA1 ligand was compound 7 (Ki = 17.7 µM), whereas compound 4 was the most potent ligand for hA3 (Ki = 2.49 µM). In addition, docking studies with hA1 and hA3 homology models were established to analyze the structure-function relationships. Results showed that the different residues located on the protein binding pocket could play an important role in ligand selectivity.

The role of drug profiles as similarity metrics: applications to repurposing, adverse effects detection and drug-drug interactions.

Explosion of the availability of big data sources along with the development in computational methods provides a useful framework to study drugs' actions, such as interactions with pharmacological targets and off-targets. Databases related to protein interactions, adverse effects and genomic profiles are available to be used for the construction of computational models. In this article, we focus on the description of biological profiles for drugs that can be used as a system to compare similarity and create methods to predict and analyze drugs' actions. We highlight profiles constructed with different biological data, such as target-protein interactions, gene expression measurements, adverse effects and disease profiles. We focus on the discovery of new targets or pathways for drugs already in the pharmaceutical market, also called drug repurposing, in the interaction with off-targets responsible for adverse reactions and in drug-drug interaction analysis. The current and future applications, strengths and challenges facing all these methods are also discussed. Biological profiles or signatures are an important source of data generation to deeply analyze biological actions with important implications in drug-related studies.

New insights into highly potent tyrosinase inhibitors based on 3-heteroarylcoumarins: Anti-melanogenesis and antioxidant activities, and computational molecular modeling studies.

Melanogenesis is a physiological pathway for the formation of melanin. Tyrosinase catalyzes the first step of this process and down-regulation of its activity is responsible for the inhibition of melanogenesis. The search for molecules capable of controlling hyperpigmentation is a trend topic in health and cosmetics. A series of heteroarylcoumarins have been synthesized and evaluated. Compounds 4 and 8 exhibited higher tyrosinase inhibitory activities (IC50=0.15 and 0.38µM, respectively), than the reference compound, kojic acid (IC50=17.9µM). Compound 4 acts as competitive, while compound 8 as uncompetitive inhibitor of mushroom tyrosinase. Furthermore, compounds 2 and 8 inhibited tyrosinase activity and melanin production in B16F10 cells. In addition, compounds 2-4 and 8 proved to have an interesting antioxidant profile in both ABTS and DPPH radicals scavenging assays. Docking experiments were carried out in order to study the interactions between these heteroarylcoumarins and mushroom tyrosinase.

Furvina inhibits the 3-oxo-C12-HSL-based quorum sensing system of Pseudomonas aeruginosa and QS-dependent phenotypes.

Disruption of cell-cell communication or quorum sensing (QS) is considered a stimulating approach for reducing bacterial pathogenicity and resistance. Although several QS inhibitors (QSIs) have been discovered so far their clinical use remains distant. This problem can be circumvented by searching for QSI among drugs already approved for the treatment of different diseases. In this context, antibiotics have earned special attention. Whereas at high concentrations antibiotics exert a killing effect, at lower concentrations they may act as signaling molecules and as such can modulate gene expression. In this study, the antibiotic furvina was shown to be able to cause inhibition of the 3-oxo-C12-HSL-dependent QS system of Pseudomonas aeruginosa. Furvina interacts with the LasI/LasR system. The data were validated by modeling studies. Furvina can also reduce biofilm formation and decrease the production of QS-controlled virulence factors.

Potent and selective MAO-B inhibitory activity: amino- versus nitro-3-arylcoumarin derivatives.

In this study we synthesized and evaluated a new series of amino and nitro 3-arylcoumarins as hMAO-A and hMAO-B inhibitors. Compounds 2, 3, 5 and 6 presented a better activity and selectivity profile against the hMAO-B isoform (IC50 values between 2 and 6nM) than selegiline. In general, the amino derivatives (4-6) proved to be more selective against MAO-B than the nitro derivatives (1-3). Additionally, a theoretical study of some physicochemical properties, PAMPA and reversibility assays for the most potent derivative, and molecular docking simulations were carried out to further explain the pharmacological results, and to identify the hypothetical binding mode for the compounds inside the hMAO-B.

Toward a complete dataset of drug-drug interaction information from publicly available sources.

Although potential drug-drug interactions (PDDIs) are a significant source of preventable drug-related harm, there is currently no single complete source of PDDI information. In the current study, all publically available sources of PDDI information that could be identified using a comprehensive and broad search were combined into a single dataset. The combined dataset merged fourteen different sources including 5 clinically-oriented information sources, 4 Natural Language Processing (NLP) Corpora, and 5 Bioinformatics/Pharmacovigilance information sources. As a comprehensive PDDI source, the merged dataset might benefit the pharmacovigilance text mining community by making it possible to compare the representativeness of NLP corpora for PDDI text extraction tasks, and specifying elements that can be useful for future PDDI extraction purposes. An analysis of the overlap between and across the data sources showed that there was little overlap. Even comprehensive PDDI lists such as DrugBank, KEGG, and the NDF-RT had less than 50% overlap with each other. Moreover, all of the comprehensive lists had incomplete coverage of two data sources that focus on PDDIs of interest in most clinical settings. Based on this information, we think that systems that provide access to the comprehensive lists, such as APIs into RxNorm, should be careful to inform users that the lists may be incomplete with respect to PDDIs that drug experts suggest clinicians be aware of. In spite of the low degree of overlap, several dozen cases were identified where PDDI information provided in drug product labeling might be augmented by the merged dataset. Moreover, the combined dataset was also shown to improve the performance of an existing PDDI NLP pipeline and a recently published PDDI pharmacovigilance protocol. Future work will focus on improvement of the methods for mapping between PDDI information sources, identifying methods to improve the use of the merged dataset in PDDI NLP algorithms, integrating high-quality PDDI information from the merged dataset into Wikidata, and making the combined dataset accessible as Semantic Web Linked Data.

Development of novel adenosine receptor ligands based on the 3-amidocoumarin scaffold.

With the aim of finding new adenosine receptor (AR) ligands presenting the 3-amidocoumarin scaffold, a study focusing on the discovery of new chemical entities was carried out. The synthesized compounds 1-8 were evaluated in radioligand binding (A1, A2A and A3) and adenylyl cyclase activity (A2B) assays in order to determine their affinity for human AR subtypes. The 3-benzamide derivative 4 showed the highest affinity of the whole series and was more than 30-fold selective for the A3 AR (Ki=3.24 µM). The current study supported that small structural changes in this scaffold allowed modulating the affinity resulting in novel promising classes of A1, A2A, and/or A3 AR ligands. We also performed docking calculations in hA2A and hA3 to identify the hypothetical binding mode for the most active compounds. In addition, some ADME properties were calculated in order to better understand the potential of these compounds as drug candidates.

In silico screening for agonists and blockers of the ß(2) adrenergic receptor: implications of inactive and activated state structures.

Ten crystal structures of the ß(2) adrenergic receptor have been published, reflecting different signaling states. Here, through controlled-docking experiments, we examined the implications of using inactive or activated structures on the in silico screening for agonists and blockers of the receptor. Specifically, we targeted the crystal structures solved in complex with carazolol (2RH1), the neutral antagonist alprenalol, the irreversible agonist FAUC50 (3PDS), and the full agonist BI-167017 (3P0G). Our results indicate that activated structures favor agonists over blockers, whereas inactive structures favor blockers over agonists. This tendency is more marked for activated than for inactive structures. Additionally, agonists tend to receive more favorable docking scores when docked at activated rather than inactive structures, while blockers do the opposite. Hence, the difference between the docking scores attained with an activated and an inactive structure is an excellent means for the classification of ligands into agonists and blockers as we determined through receiver operating characteristic curves and linear discriminant analysis. With respect to virtual screening, all structures prioritized well agonists and blockers over nonbinders. However, inactive structures worked better for blockers and activated structures worked better for agonists, respectively. Notably, the combination of individual docking experiments through receptor ensemble docking resulted in an excellent performance in the retrieval of both agonists and blockers. Finally, we demonstrated that the induced-fit docking of agonists is a viable way of modifying an inactive crystal structure and bias it toward the in silico recognition of agonists rather than blockers.

One Year of SARS-CoV-2: How Much Has the Virus Changed?

Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) has caused a worldwide crisis with profound effects on both public health and the economy. In order to combat the COVID-19 pandemic, research groups have shared viral genome sequence data through the Global Initiative on Sharing All Influenza Data (GISAID). Over the past year, ≈290,000 full SARS-CoV-2 proteome sequences have been deposited in the GISAID. Here, we used these sequences to assess the rate of nonsynonymous mutants over the entire viral proteome. Our analysis shows that SARS-CoV-2 proteins are mutating at substantially different rates, with most of the viral proteins exhibiting little mutational variability. As anticipated, our calculations capture previously reported mutations that arose in the first months of the pandemic, such as D614G (Spike), P323L (NSP12), and R203K/G204R (Nucleocapsid), but they also identify more recent mutations, such as A222V and L18F (Spike) and A220V (Nucleocapsid), among others. Our comprehensive temporal and geographical analyses show two distinct periods with different proteome mutation rates: December 2019 to July 2020 and August to December 2020. Notably, some mutation rates differ by geography, primarily during the latter half of 2020 in Europe. Furthermore, our structure-based molecular analysis provides an exhaustive assessment of SARS-CoV-2 mutation rates in the context of the current set of 3D structures available for SARS-CoV-2 proteins. This emerging sequence-to-structure insight is beginning to illuminate the site-specific mutational (in)tolerance of SARS-CoV-2 proteins as the virus continues to spread around the globe.

Ligand and structure-based models for the prediction of ligand-receptor affinities and virtual screenings: Development and application to the beta(2)-adrenergic receptor.

In this study, we evaluated the applicability of ligand-based and structure-based models to quantitative affinity predictions and virtual screenings for ligands of the beta(2)-adrenergic receptor, a G protein-coupled receptor (GPCR). We also devised and evaluated a number of consensus models obtained through partial least square regressions, to combine the strengths of the individual components. In all cases, the bioactive conformation of each ligand was derived from molecular docking at the crystal structure of the receptor. We identified the most effective models applicable to the different scenarios, in the presence or in the absence of a training set. For ranking the affinity of closely related analogs when a training set is available, a ligand-based consensus model (LI-CM) seems to be the best choice, while the structure-based MM-GBSA score seems the best alternative in the absence of a training set. For virtual screening purposes, the structure-based MM-GBSA score was found to be the method of choice. Consensus models consistently had performances superior or close to those of the best individual components, and were endowed with a significantly increased robustness. Given multiple models with no a priori knowledge of their predictive capabilities, constructing a consensus model ensures results very close to those that the best model alone would have yielded.

Structure-Based Optimization of Coumarin hA3 Adenosine Receptor Antagonists.

Adenosine receptors participate in many physiological functions. Molecules that may selectively interact with one of the receptors are favorable multifunctional chemical entities to treat or decelerate the evolution of different diseases. 3-Arylcoumarins have already been studied as neuroprotective agents by our group. Here, differently 8-substituted 3-arylcoumarins are complementarily studied as ligands of adenosine receptors, performing radioligand binding assays. Among the synthesized compounds, selective A3 receptor antagonists were found. 3-(4-Bromophenyl)-8-hydroxycoumarin (compound 4) displayed the highest potency and selectivity as A3 receptor antagonist (Ki = 258 nM). An analysis of its X-ray diffraction provided detailed information on its structure. Further evaluation of a selected series of compounds indicated that it is the nature and position of the substituents that determine their activity and selectivity. Theoretical modeling calculations corroborate and explain the experimental data, suggesting this novel scaffold can be involved in the generation of candidates as multitarget drugs.

A network-QSAR model for prediction of genetic-component biomarkers in human colorectal cancer.

The combination of the network theory and the calculation of topological indices (TIs) allow establishing relationships between the molecular structure of large molecules like the genes and proteins and their properties at a biological level. This type of models can be considered quantitative structure-activity relationships (QSAR) for biopolymers. In the present work a QSAR model is reported for proteins, related to human colorectal cancer (HCC) and codified by different genes that have been identified experimentally by Sjöblom et al. [2006. The consensus coding sequences of human breast and colorectal cancers. Science 314, 268-274] among more than 10000 human genes. The 69 proteins related to human colorectal cancer (HCCp) and a control group of 200 proteins not related to HCC (no-HCCp) were represented through an HP Lattice type Network. Starting from the generated graphs we calculate a set of descriptors of electrostatic potential type (xi(k)) that allow to establish, through a linear discriminant analysis (LDA), a QSAR model of relatively high percentage of good classification (higher than 80%) to differentiate between HCCp and no-HCCp proteins. The purpose of this study is helping to predict the possible implication of a certain gene and/or protein (biomarker) in the colorectal cancer. Different procedures of validation of the obtained model have been carried out in order to corroborate its stability, including cross-validation series (CV) and evaluation of an additional series of 200 no-HCCp. This biostatistic methodology could be applied to predict human colorectal cancer biomarkers and to understand much better the biological aspects of this disease.

Structural basis of the selectivity of the beta(2)-adrenergic receptor for fluorinated catecholamines.

The important and diverse biological functions of adrenergic receptors, a subclass of G protein-coupled receptors (GPCRs), have made the search for compounds that selectively stimulate or inhibit the activity of different adrenergic receptor subtypes an important area of medicinal chemistry. We previously synthesized 2-, 5-, and 6-fluoronorepinehprine (FNE) and 2-, 5-, and 6-fluoroepinephrine (FEPI) and found that 2FNE and 2FEPI were selective beta-adrenergic agonists and that 6FNE and 6FEPI were selective alpha-adrenergic agonists, while 5FNE and 5FEPI were unselective. Agonist potencies correlated well with receptor binding affinities. Here, through a combination of molecular modeling and site-directed mutagenesis, we have identified N293 in the beta(2)-adrenergic receptor as a crucial residue for the selectivity of the receptor for catecholamines fluorinated at different positions.

In Silico Prediction of P-glycoprotein Binding: Insights from Molecular Docking Studies.

The P-glycoprotein is an efflux transporter that expels substances out of the cells and has an important impact on the pharmacokinetic and pharmacodynamic properties of drugs. The study of the interactions between ligands and the P-glycoprotein has implications in the design of Central Nervous System drugs and their transport across the blood-brain barrier. Moreover, since the P-glycoprotein is overexpressed in some types of cancers, the protein is responsible for expelling the drug therapies from the cells, and hence, for drug resistance. In this review, we describe different P-glycoprotein binding sites reported for substrates, inhibitors and modulators, and focus on molecular docking studies that provide useful information about drugs and P-glycoprotein interactions. Docking in crystallized structures and homology models showed potential in the detection of the binding site and key residues responsible for ligand recognition. Moreover, virtual screening through molecular docking discriminates P-glycoprotein ligands from decoys. We also discuss challenges and limitations of molecular docking simulations applied to this particular protein. Computational structure-based approaches are very helpful in the study of novel ligands that interact with the P-glycoprotein and provide insights to understand the P-glycoprotein molecular mechanism of action.

QSAR model for alignment-free prediction of human breast cancer biomarkers based on electrostatic potentials of protein pseudofolding HP-lattice networks.

Network theory allows relationships to be established between numerical parameters that describe the molecular structure of genes and proteins and their biological properties. These models can be considered as quantitative structure-activity relationships (QSAR) for biopolymers. The work described here concerns the first QSAR model for 122 proteins that are associated with human breast cancer (HBC), as identified experimentally by Sjöblom et al. (Science 2006, 314, 268) from over 10,000 human proteins. In this study, the 122 proteins related to HBC (HBCp) and a control group of 200 proteins that are not related to HBC (non-HBCp) were forced to fold in an HP lattice network. From these networks a series of electrostatic potential parameters (xi(k)) was calculated to describe each protein numerically. The use of xi(k) as an entry point to linear discriminant analysis led to a QSAR model to discriminate between HBCp and non-HBCp, and this model could help to predict the involvement of a certain gene and/or protein in HBC. In addition, validation procedures were carried out on the model and these included an external prediction series and evaluation of an additional series of 1000 non-HBCp. In all cases good levels of classification were obtained with values above 80%. This study represents the first example of a QSAR model for the computational chemistry inspired search of potential HBC protein biomarkers.

Ligand and Structure-based Modeling of Passive Diffusion through the Blood-Brain Barrier.

BACKGROUND: Blood-brain barrier transport is an important process to be considered in drug candidates. The blood-brain barrier protects the brain from toxicological agents and, therefore, also establishes a restrictive mechanism for the delivery of drugs into the brain. Although there are different and complex mechanisms implicated in drug transport, in this review we focused on the prediction of passive diffusion through the blood-brain barrier. METHODS: We elaborated on ligand-based and structure-based models that have been described to predict the blood-brain barrier permeability. RESULTS: Multiple 2D and 3D QSPR/QSAR models and integrative approaches have been published to establish quantitative and qualitative relationships with the blood-brain barrier permeability. We explained different types of descriptors that correlate with passive diffusion along with data analysis methods. Moreover, we discussed the applicability of other types of molecular structure-based simulations, such as molecular dynamics, and their implications in the prediction of passive diffusion. Challenges and limitations of experimental measurements of permeability and in silico predictive methods were also described. CONCLUSION: Improvements in the prediction of blood-brain barrier permeability from different types of in silico models are crucial to optimize the process of Central Nervous System drug discovery and development.