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.

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.

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.

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.

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.

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.

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.

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 Aß-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.

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.

Coumarins and adenosine receptors: New perceptions in structure-affinity relationships.

Adenosine receptor (AR) subtypes are involved in several physiological and pharmacological processes. Ligands that are able to selectively modulate one receptor subtype can delay or slow down the progression of diverse diseases. In this context, our research group focused its investigation into the discovery and development of novel, potent and selective AR ligands based on coumarin scaffold. Therefore, a series of 3-phenylcarboxamidocoumarins were synthesized and their affinity for the human AR subtypes was screened by radioligand binding assays for A1 , A2A and A3 receptors and for A2B by adenylyl cyclase assay. Compound 26 was found to be the most remarkable, with a hA1 /hA3 and hA2A /hA3 selectivity of 42, for the A3 AR (Ki  = 2.4 µm). Receptor-driven molecular modelling studies have provided valuable information on the binding/selectivity data of compound 26 and for the following optimization process. Moreover, compound 26 presents drug-like properties according to the general guidelines linked to the concept.

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.

Synthesis and structure-activity relationship study of novel 3-heteroarylcoumarins based on pyridazine scaffold as selective MAO-B inhibitors.

Compounds of hybrid structure pyridazine-coumarin were discovered as potent, selective and reversible inhibitors of monoamine oxidase B (MAO-B). These compounds were synthesized in good yield following a multistep approach based on Knoevenagel reaction and using as key intermediate pyridazinone 16, which was obtained from maleic anhydride and furan. Compounds 9b and 9d are the most active compounds of these series, with IC50 values in the sub-micromolar range, and lack of cytotoxic effects. Theoretical calculation of ADME properties also suggested a good pharmacokinetic profile for both compounds. Docking simulations provided insights into enzyme inhibitor interactions and allowed us to rationalize the observed structure-activity relationships (SARs).

Molecular Docking and Drug Discovery in ß-Adrenergic Receptors.

BACKGROUND: Evolution in computer engineering, availability of increasing amounts of data and the development of new and fast docking algorithms and software have led to improved molecular simulations with crucial applications in virtual high-throughput screening and drug discovery. Moreover, analysis of protein-ligand recognition through molecular docking has become a valuable tool in drug design. OBJECTIVE: In this review, we focus on the applicability of molecular docking on a particular class of G protein-coupled receptors: the ß-adrenergic receptors, which are relevant targets in clinic for the treatment of asthma and cardiovascular diseases. RESULTS: We describe the binding site in ß-adrenergic receptors to understand key factors in ligand recognition along with the proteins activation process. Moreover, we focus on the discovery of new lead compounds that bind the receptors, on the evaluation of virtual screening using the active/ inactive binding site states, and on the structural optimization of known families of binders to improve ß-adrenergic affinity. We also discussed strengths and challenges related to the applicability of molecular docking in ß-adrenergic receptors. CONCLUSION: Molecular docking is a valuable technique in computational chemistry to deeply analyze ligand recognition and has led to important breakthroughs in drug discovery and design in the field of ß-adrenergic receptors.

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.

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.

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.

Computational Drug Target Screening through Protein Interaction Profiles.

The development of computational methods to discover novel drug-target interactions on a large scale is of great interest. We propose a new method for virtual screening based on protein interaction profile similarity to discover new targets for molecules, including existing drugs. We calculated Target Interaction Profile Fingerprints (TIPFs) based on ChEMBL database to evaluate drug similarity and generated new putative compound-target candidates from the non-intersecting targets in each pair of compounds. A set of drugs was further studied in monoamine oxidase B (MAO-B) and cyclooxygenase-1 (COX-1) enzyme through molecular docking and experimental assays. The drug ethoxzolamide and the natural compound piperlongumine, present in Piper longum L, showed hMAO-B activity with IC50 values of 25 and 65 µM respectively. Five candidates, including lapatinib, SB-202190, RO-316233, GW786460X and indirubin-3'-monoxime were tested against human COX-1. Compounds SB-202190 and RO-316233 showed a IC50 in hCOX-1 of 24 and 25 µM respectively (similar range as potent inhibitors such as diclofenac and indomethacin in the same experimental conditions). Lapatinib and indirubin-3'-monoxime showed moderate hCOX-1 activity (19.5% and 28% of enzyme inhibition at 25 µM respectively). Our modeling constitutes a multi-target predictor for large scale virtual screening with potential in lead discovery, repositioning and drug safety.

Leveraging 3D chemical similarity, target and phenotypic data in the identification of drug-protein and drug-adverse effect associations.

BACKGROUND: Drug-target identification is crucial to discover novel applications for existing drugs and provide more insights about mechanisms of biological actions, such as adverse drug effects (ADEs). Computational methods along with the integration of current big data sources provide a useful framework for drug-target and drug-adverse effect discovery. RESULTS: In this article, we propose a method based on the integration of 3D chemical similarity, target and adverse effect data to generate a drug-target-adverse effect predictor along with a simple leveraging system to improve identification of drug-targets and drug-adverse effects. In the first step, we generated a system for multiple drug-target identification based on the application of 3D drug similarity into a large target dataset extracted from the ChEMBL. Next, we developed a target-adverse effect predictor combining targets from ChEMBL with phenotypic information provided by SIDER data source. Both modules were linked to generate a final predictor that establishes hypothesis about new drug-target-adverse effect candidates. Additionally, we showed that leveraging drug-target candidates with phenotypic data is very useful to improve the identification of drug-targets. The integration of phenotypic data into drug-target candidates yielded up to twofold precision improvement. In the opposite direction, leveraging drug-phenotype candidates with target data also yielded a significant enhancement in the performance. CONCLUSIONS: The modeling described in the current study is simple and efficient and has applications at large scale in drug repurposing and drug safety through the identification of mechanism of action of biological effects.

Feasibility of Prioritizing Drug-Drug-Event Associations Found in Electronic Health Records.

BACKGROUND AND OBJECTIVE: Several studies have demonstrated the ability to detect adverse events potentially related to multiple drug exposure via data mining. However, the number of putative associations produced by such computational approaches is typically large, making experimental validation difficult. We theorized that those potential associations for which there is evidence from multiple complementary sources are more likely to be true, and explored this idea using a published database of drug-drug-adverse event associations derived from electronic health records (EHRs). METHODS: We prioritized drug-drug-event associations derived from EHRs using four sources of information: (1) public databases, (2) sources of spontaneous reports, (3) literature, and (4) non-EHR drug-drug interaction (DDI) prediction methods. After pre-filtering the associations by removing those found in public databases, we devised a ranking for associations based on the support from the remaining sources, and evaluated the results of this rank-based prioritization. RESULTS: We collected information for 5983 putative EHR-derived drug-drug-event associations involving 345 drugs and ten adverse events from four data sources and four prediction methods. Only seven drug-drug-event associations (<0.5 %) had support from the majority of evidence sources, and about one third (1777) had support from at least one of the evidence sources. CONCLUSIONS: Our proof-of-concept method for scoring putative drug-drug-event associations from EHRs offers a systematic and reproducible way of prioritizing associations for further study. Our findings also quantify the agreement (or lack thereof) among complementary sources of evidence for drug-drug-event associations and highlight the challenges of developing a robust approach for prioritizing signals of these associations.

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.