FAF-Drugs3: a web server for compound property calculation and chemical library design

Unveiling Arformoterol as a potent LSD1 inhibitor for breast cancer treatment: A comprehensive study integrating 3D-QSAR pharmacophore modeling, molecular docking, molecular dynamics simulations and in vitro assays

Lysine Specific Demethylase 1 (LSD1) has been identified as a chromatin-modifying enzyme implicated in various cancer pathogeneses, highlighting the potential for novel epigenetic cancer treatments through the development of effective inhibitors. We employed 3D-QSAR pharmacophore modeling, molecular docking, and molecular dynamics simulations to identify a promising drug candidate for LSD1 inhibition. RMSD, RMSF, H-bond, and DSSP analysis demonstrated that ZINC02599970 (Arformoterol) and ZINC13453966 exhibited the highest LSD1 inhibitory potential. Experimental validation using MCF-7 and MDA-MB-231 cell lines revealed that Arformoterol displayed potent antiproliferative activity with IC50 values of 12.30 ± 1.48 μM and 19.69 ± 1.15 μM respectively. In contrast, the IC50 values obtained for the control (tranylcypromine) in exposure to MCF-7 and MDA-MB-231 cells were 104.6 ± 1.69 μM and 77 ± 0.67 μM, respectively. Arformoterol demonstrated greater LSD1 inhibitory potency in MCF-7 cells compared to MDA-MB-231 cells. Also, the expression of genes involved in chromatin rearrangement (LSD1), angiogenesis (VEGF1), cell migration (RORα), signal transduction (S100A8), apoptosis, and cell cycle (p53) were investigated. Arformoterol enhanced apoptosis and induced cell cycle arrest at the G2/M phase, both in MCF-7 and MDA-MB-231 cancer cells. Based on our findings, we propose that Arformoterol represents a promising candidate for breast cancer treatment, owing to its potent LSD1 inhibitory activity.

Unveiling Novel ERCC1-XPF Complex Inhibitors: Bridging the Gap from In Silico Exploration to Experimental Design

Modifications in DNA repair pathways are recognized as prognostic markers and potential therapeutic targets in various cancers, including non-small cell lung cancer (NSCLC). Overexpression of ERCC1 correlates with poorer prognosis and response to platinum-based chemotherapy. As a result, there is a pressing need to discover new inhibitors of the ERCC1-XPF complex that can potentiate the efficacy of cisplatin in NSCLC. In this study, we developed a structure-based virtual screening strategy targeting the inhibition of ERCC1 and XPF interaction. Analysis of crystal structures and a library of small molecules known to act against the complex highlighted the pivotal role of Phe293 (ERCC1) in maintaining complex stability. This residue was chosen as the primary binding site for virtual screening. Using an optimized docking protocol, we screened compounds from various databases, ultimately identifying more than one hundred potential inhibitors. Their capability to amplify cisplatin-induced cytotoxicity was assessed in NSCLC H1299 cells, which exhibited the highest ERCC1 expression of all the cell lines tested. Of these, 22 compounds emerged as promising enhancers of cisplatin efficacy. Our results underscore the value of pinpointing crucial molecular characteristics in the pursuit of novel modulators of the ERCC1-XPF interaction, which could be combined with cisplatin to treat NSCLC more effectively.

Identification of selective 5-LOX and FLAP inhibitors as novel anti-inflammatory agents by ligand-based virtual screening

Inflammation is a multifaceted biological process in which the conversion of arachidonic acid to eicosanoids, including prostaglandins and leukotrienes (LTs), plays a crucial role. 5-Lipoxygenase (5-LOX) is a key enzyme in cellular LT biosynthesis, and it is supported by the accessory protein 5-lipoxygenase-activating protein (FLAP). Pharmacological interventions to modulate LTs aim at either decreasing their biosynthesis or at mitigating their biological effects. Therefore, inhibiting 5-LOX or FLAP represents a useful strategy to reduce inflammation. Herein we present the identification and pharmacological evaluation of novel inhibitors targeting 5-LOX or FLAP. By means of a ligand-based virtual screening approach, we selected 38 compounds for in vitro assays. Among them, ALR-38 exhibits direct 5-LOX inhibition, while ALR-6 and ALR-27 showed potential as FLAP inhibitors. These latter not only reduced LT production but also promoted the generation of specialized pro-resolving mediators in specific human macrophage phenotypes. Interestingly, the identified compounds turned out to be selective for their respective targets, as none of them displayed activity towards microsomal prostaglandin E2 synthase-1 and soluble epoxide hydrolase, which are other proteins involved in eicosanoid biosynthesis. Thus, these compounds are endowed with potential therapeutic utility in mitigating inflammatory responses and might offer a venue for tackling inflammation-based disorders.

Discovery of PLD4 modulators by high-throughput screening and kinetic analysis

Phospholipase D3 (PLD3) and D4 (PLD4) are endolysosomal exonucleases of ssDNA and ssRNA that regulate innate immunity. Polymorphisms of these enzymes are correlated with numerous human diseases, including Alzheimer's, rheumatoid arthritis, and systemic sclerosis. Pharmacological modulation of these immunoregulatory proteins may yield novel immunotherapies and adjuvants. A previous study reported a high-throughput screen (N = 17,952) that discovered a PLD3-selective activator and inhibitor, as well as a nonselective inhibitor, but failed to identify selective modulators of PLD4. However, modulators selective for PLD4 are therapeutically pertinent, since recent reports have shown that regulating this protein has direct implications in cancer and autoimmune diseases. Furthermore, the high expression of PLD4 in dendritic and myeloid cells, in comparison to the broader expression of PLD3, presents the opportunity for a cell-targeted immunotherapy. Here, we describe screening of an expended diversity library (N = 45,760) with an improved platform and report the discovery of one inhibitor and three activators selective for PLD4. Furthermore, kinetic modeling and structural analysis suggest mechanistic differences in the modulation of these hits. These findings further establish the utility of this screening platform and provide a set of chemical scaffolds to guide future small-molecule development for this novel immunoregulator target.

Multifaceted approach toward mapping out the anticancer properties of small molecules via in vitro evaluation on melanoma and nonmelanoma skin cancer cells, and in silico target fishing

Melanoma and nonmelanoma skin cancers are among the most prevalent and most lethal forms of skin cancers. To identify new lead compounds with potential anticancer properties for further optimization, in vitro assays combined with in-silico target fishing and docking have been used to identify and further map out the antiproliferative and potential mode of action of molecules from a small library of compounds previously prepared in our laboratory. From screening these compounds in vitro against A375, SK-MEL-28, A431, and SCC-12 skin cancer cell lines, 35 displayed antiproliferative activities at the micromolar level, with the majority being primarily potent against the A431 and SCC-12 squamous carcinoma cell lines. The most active compounds 11 (A431: IC50  = 5.0 μM, SCC-12: IC50  = 2.9 μM, SKMEL-28: IC50  = 4.9 μM, A375: IC50  = 6.7 μM) and 13 (A431: IC50  = 5.0 μM, SCC-12: IC50  = 3.3 μM, SKMEL-28: IC50  = 13.8 μM, A375: IC50  = 17.1 μM), significantly and dose-dependently induced apoptosis of SCC-12 and SK-MEL-28 cells, as evidenced by the suppression of Bcl-2 and upregulation of Bax, cleaved caspase-3, caspase-9, and PARP protein expression levels. Both agents significantly reduced scratch wound healing, colony formation, and expression levels of deregulated cancer molecular targets including RSK/Akt/ERK1/2 and S6K1. In silico target prediction and docking studies using the SwissTargetPrediction web-based tool suggested that CDK8, CLK4, nuclear receptor ROR, tyrosine protein-kinase Fyn/LCK, ROCK1/2, and PARP, all of which are dysregulated in skin cancers, might be prospective targets for the two most active compounds. Further validation of these targets by western blot analyses, revealed that ROCK/Fyn and its associated Hedgehog (Hh) pathways were downregulated or modulated by the two lead compounds. In aggregate, these results provide a strong framework for further validation of the observed activities and the development of a more comprehensive structure-activity relationship through the preparation and biological evaluation of analogs.

Lead optimization study on indoline-2,3-dione derivatives as potential fatty acid amide hydrolase inhibitors

Based on the known isatin-based fatty acid amide hydrolase (FAAH) inhibitor BSS-7, we designed and synthesized two small sets (6-13 and 17-20) of N-1 and C-3 substituted isatin derivatives and evaluated them for their in vitro FAAH inhibition properties. The lead simplification by modification of bulky aryl moiety at N-1 with a flexible allyl group produced a nanomolar (IC50 = 6.7 nM, Ki = 5 nM) inhibitor 11 (Z)-3-((1H-benzo[d]imidazol-2-yl)imino)-1-allylindolin-2-one which exhibited a reversible and competitive FAAH inhibition with 1500 times more potency to BSS-7 (1.49 ± 0.03 µM). The lead compound 11 also showed a high blood-brain permeability and a significant antioxidant profile with no neurotoxicity. Docking results suggested that the inhibitor molecules occupied the active site of FAAH and offered optimal binding interactions. A molecular dynamics simulation study ascertained the stability of the lead inhibitor 11-FAAH complex. In silico ADMET profiling studies unveiled that compound 11 possesses good drug-like properties and merits further evaluation.Communicated by Ramaswamy H. Sarma.

Lansoprazole as a potent HDAC2 inhibitor for treatment of colorectal cancer: An in-silico analysis and experimental validation

BACKGROUND

Histone deacetylase 2 (HDAC2), belonging to the class I HDAC family, holds significant therapeutic potential as a crucial target for diverse cancer types. As key players in the realm of epigenetic regulatory enzymes, histone deacetylases (HDACs) are intricately involved in the onset and progression of cancer. Consequently, pursuing isoform-specific inhibitors targeting histone deacetylases (HDACs) has garnered substantial interest in both biological and medical circles. The objective of the present investigation was to employ a drug repurposing approach to discover novel and potent HDAC2 inhibitors.

MATERIALS AND METHODS

In this study, our protocol is presented on virtual screening to identify novel potential HDAC2 inhibitors through 3D-QSAR, molecular docking, pharmacophore modeling, and molecular dynamics (MD) simulation. Afterward, In-vitro assays were employed to evaluate the cytotoxicity, apoptosis, and migration of HCT-116 cell lines under treatment of hit compound and valproic acid as a control inhibitor. The expression levels of HDAC2, TP53, BCL2, and BAX were evaluated by QRT-PCR.

RESULTS

RMSD, RMSF, H-bond, and DSSP analysis results confirmed that among bioinformatically selected compounds, lansoprazole exhibited the highest HDAC2 inhibitory potential. Experimental validation revealed that lansoprazole displayed significant antiproliferative activity. The determined IC50 value was 400 ± 2.36 μM. Furthermore, the apoptotic cells ratio concentration-dependently increased under Lansoprazole treatment. Results of the Scratch assay indicated that lansoprazole led to decreasing the migration of CRC cells. Finally, under Lansoprazole treatment the expression level of BCL2 and HDAC2 decreased and BAX and TP53 increased.

CONCLUSION

Taking together the results of the current study indicated that Lansoprazole as a novel HDAC2 inhibitor, could be used as a potential therapeutic agent for the treatment of CRC. Although, further experimental studies should be performed before using this compound in the clinic.

Identification of a Protein Arginine Methyltransferase 7 (PRMT7)/Protein Arginine Methyltransferase 9 (PRMT9) Inhibitor

Less studied than the other protein arginine methyltransferase isoforms, PRMT7 and PRMT9 have recently been identified as important therapeutic targets. Yet, most of their biological roles and functions are still to be defined, as well as the structural requirements that could drive the identification of selective modulators of their activity. We recently described the structural requirements that led to the identification of potent and selective PRMT4 inhibitors spanning both the substrate and the cosubstrate pockets. The reanalysis of the data suggested a PRMT7 preferential binding for shorter derivatives and prompted us to extend these structural studies to PRMT9. Here, we report the identification of the first potent PRMT7/9 inhibitor and its binding mode to the two PRMT enzymes. Label-free quantification mass spectrometry confirmed significant inhibition of PRMT activity in cells. We also report the setup of an effective AlphaLISA assay to screen small molecule inhibitors of PRMT9.

Alotaibi N, Alshammari N, Saeed M, Karunakaran R. Isolation of anticancer bioactive secondary metabolites from the sponge-derived endophytic fungi Penicillium sp. and in-silico computational docking approach

Introduction

Fungus-derived secondary metabolites are fascinating with biomedical potential and chemical diversity. Mining endophytic fungi for drug candidates is an ongoing process in the field of drug discovery and medicinal chemistry. Endophytic fungal symbionts from terrestrial plants, marine flora, and fauna tend to produce interesting types of secondary metabolites with biomedical importance of anticancer, antiviral, and anti-tuberculosis properties.

Methods

An organic ethyl acetate extract of Penicillium verruculosum sponge-derived endophytic fungi from Spongia officinalis yielded seven different secondary metabolites which are purified through HPLC. The isolated compounds are of averufin (1), aspergilol-A (2), sulochrin (3), monomethyl sulochrin (4), methyl emodin (5), citreorosein (6), and diorcinol (7). All the seven isolated compounds were characterized by high-resolution NMR spectral studies. All isolated compounds', such as anticancer, antimicrobial, anti-tuberculosis, and antiviral, were subjected to bioactivity screening.

Results

Out of seven tested compounds, compound (1) exhibits strong anticancer activity toward myeloid leukemia. HL60 cell lines have an IC50 concentration of 1.00μm, which is nearly significant to that of the standard anticancer drug taxol. A virtual computational molecular docking approach of averufin with HL60 antigens revealed that averufin binds strongly with the protein target alpha, beta-tubulin (1JFF), with a -10.98 binding score. Consecutive OSIRIS and Lipinski ADME pharmacokinetic validation of averufin with HL60 antigens revealed that averufin has good pharmacokinetic properties such as drug score, solubility, and mutagenic nature. Furthermore, aspergilol-A (2) is the first report on the Penicillium verruculosum fungal strain.

Discussion

We concluded that averufin (1) isolated from Penicillium verruculosum can be taken for further preliminary clinical trials like animal model in-vivo studies and pharmacodynamic studies. A future prospect of in-vivo anticancer screening of averufin can be validated through the present experimental findings.

Evaluating Scalable Supervised Learning for Synthesize-on-Demand Chemical Libraries

Traditional small-molecule drug discovery is a time-consuming and costly endeavor. High-throughput chemical screening can only assess a tiny fraction of drug-like chemical space. The strong predictive power of modern machine-learning methods for virtual chemical screening enables training models on known active and inactive compounds and extrapolating to much larger chemical libraries. However, there has been limited experimental validation of these methods in practical applications on large commercially available or synthesize-on-demand chemical libraries. Through a prospective evaluation with the bacterial protein-protein interaction PriA-SSB, we demonstrate that ligand-based virtual screening can identify many active compounds in large commercial libraries. We use cross-validation to compare different types of supervised learning models and select a random forest (RF) classifier as the best model for this target. When predicting the activity of more than 8 million compounds from Aldrich Market Select, the RF substantially outperforms a naïve baseline based on chemical structure similarity. 48% of the RF's 701 selected compounds are active. The RF model easily scales to score one billion compounds from the synthesize-on-demand Enamine REAL database. We tested 68 chemically diverse top predictions from Enamine REAL and observed 31 hits (46%), including one with an IC50 value of 1.3 μM.

Bile acid metabolism and signaling: Emerging pharmacological targets of dietary polyphenols

Beyond their role as emulsifiers of lipophilic compounds, bile acids (BAs) are signaling endocrine molecules that show differential affinity and specificity for a variety of canonical and non-canonical BA receptors. Primary BAs (PBAs) are synthesized in the liver while secondary BAs (SBAs) are gut microbial metabolites of PBA species. PBAs and SBAs signal to BA receptors that regulate downstream pathways of inflammation and energy metabolism. Dysregulation of BA metabolism or signaling has emerged as a feature of chronic disease. Dietary polyphenols are non-nutritive plant-derived compounds associated with decreased risk of metabolic syndrome, type-2 diabetes, hepatobiliary and cardiovascular disease. Evidence suggests that the health promoting effects of dietary polyphenols are linked to their ability to alter the gut microbial community, the BA pool, and BA signaling. In this review we provide an overview of BA metabolism and summarize studies that link the cardiometabolic improvements of dietary polyphenols to their modulation of BA metabolism and signaling pathways, and the gut microbiota. Finally, we discuss approaches and challenges in deciphering cause-effect relationships between dietary polyphenols, BAs, and gut microbes.

Improving on in-silico prediction of oral drug bioavailability

INTRODUCTION

Although significant development has been made in high-throughput screening of oral drug absorption and oral bioavailability, in silico prediction continues to play an important role in prediction of oral bioavailability and assisting in the proper selection of potential drug candidates.

AREAS COVERED

This review describes the improvements and latest modeling methods and algorithms available for the prediction of this important parameter. We performed a PubMed database search with a focus on the literature published in the last 15 years.

EXPERT OPINION

A tremendous efforts have been done in the past several years to develop reliable prediction tools that can provide accurate prediction for oral bioavailability. Several studies demonstrated new methodologies and techniques to develop either web-based in silico predictive tools or integrated PBPK models to predict oral bioavailability for new molecules. Improvements in the databases and the computational power will enhance the in silico prediction accuracy and reliability. Finally, introducing artificial intelligence to the drug development process will help improve the prediction tools.

Exhaustive in silico design and screening of novel antipsychotic compounds with improved pharmacodynamics and blood-brain barrier permeation properties

Antipsychotic drugs or neuroleptics are widely used in the treatment of psychosis as a manifestation of schizophrenia and bipolar disorder. However, their effectiveness largely depends on the blood-brain barrier (BBB) permeation (pharmacokinetics) and drug-receptor pharmacodynamics. Therefore, in this study, we developed and implemented the in silico pipeline to design novel compounds (n = 260) as leads using the standard drug scaffolds with improved PK/PD properties from the standard scaffolds. As a result, the best candidates (n = 3) were evaluated in molecular docking to interact with serotonin and dopamine receptors. Finally, haloperidol (HAL) derivative (1-(4-fluorophenyl)-4-(4-hydroxy-4-{4-[(2-phenyl-1,3-thiazol-4-yl)methyl]phenyl}piperidin-1-yl)butan-1-one) was identified as a "magic shotgun" lead compound with better affinity to the 5-HT2A, 5-HT1D, D2, D3, and 5-HT1B receptors than the control molecule. Additionally, this hit substance was predicted to possess similar BBB permeation properties and much lower toxicological profiles in comparison to HAL. Overall, the proposed rational drug design platform for novel antipsychotic drugs based on the BBB permeation and receptor binding might be an invaluable asset for a medicinal chemist or translational pharmacologist.Communicated by Ramaswamy H. Sarma.

Computational approaches for the design of modulators targeting protein-protein interactions

BACKGROUND

Protein-protein interactions (PPIs) are intriguing targets for designing novel small-molecule inhibitors. The role of PPIs in various infectious and neurodegenerative disorders makes them potential therapeutic targets . Despite being portrayed as undruggable targets, due to their flat surfaces, disorderedness, and lack of grooves. Recent progresses in computational biology have led researchers to reconsider PPIs in drug discovery.

AREAS COVERED

In this review, we introduce in-silico methods used to identify PPI interfaces and present an in-depth overview of various computational methodologies that are successfully applied to annotate the PPIs. We also discuss several successful case studies that use computational tools to understand PPIs modulation and their key roles in various physiological processes.

EXPERT OPINION

Computational methods face challenges due to the inherent flexibility of proteins, which makes them expensive, and result in the use of rigid models. This problem becomes more significant in PPIs due to their flexible and flat interfaces. Computational methods like molecular dynamics (MD) simulation and machine learning can integrate the chemical structure data into biochemical and can be used for target identification and modulation. These computational methodologies have been crucial in understanding the structure of PPIs, designing PPI modulators, discovering new drug targets, and predicting treatment outcomes.

Evaluation of Free Online ADMET Tools for Academic or Small Biotech Environments

For a new molecular entity (NME) to become a drug, it is not only essential to have the right biological activity also be safe and efficient, but it is also required to have a favorable pharmacokinetic profile including toxicity (ADMET). Consequently, there is a need to predict, during the early stages of development, the ADMET properties to increase the success rate of compounds reaching the lead optimization process. Since Lipinski's rule of five, the prediction of pharmacokinetic parameters has evolved towards the current in silico tools based on empirical approaches or molecular modeling. The commercial specialized software for performing such predictions, which is usually costly, is, in many cases, not among the possibilities for research laboratories in academia or at small biotech companies. Nevertheless, in recent years, many free online tools have become available, allowing, more or less accurately, for the prediction of the most relevant pharmacokinetic parameters. This paper studies 18 free web servers capable of predicting ADMET properties and analyzed their advantages and disadvantages, their model-based calculations, and their degree of accuracy by considering the experimental data reported for a set of 24 FDA-approved tyrosine kinase inhibitors (TKIs) as a model of a research project.

Pyrazoline scaffold: hit identification to lead synthesis and biological evaluation as antidiabetic agents

Background: Mining of novel scaffolds as potential DPP-IV inhibitors for future development of potential candidates as antidiabetic agents to address global issues. Methodology: The identified hit KB-10 from a previously reported study was taken as a lead for designing a library of analogues and screened initially based on in silico parameters and docking score. A series of selected (2[4-(1-acetyl-5-phenyl-4,5-dihydro-1H-pyrazol-3-yl)phenoxy]-1-phenylethanone derivatives were synthesized and evaluated through in vitro studies. Compounds KB-23, KB-22 and KB-06 were found to be as potent, with IC₅₀ values of 0.10 μM, 0.12 μM and 0.35 μM, respectively. They also showed promising antihyperglycemic potential in in vivo studies (oral glucose tolerance tests) in Wistar rats. Conclusion: This work establishes pyrazoline analogues KB-23, KB-22 and KB-06 as promising starting points for the development of potential antidiabetic agents.

Synthesis of Unsymmetrical Squaramides as Allosteric GSK-3β Inhibitors Promoting β-Catenin-Mediated Transcription of TCF/LEF in Retinal Pigment Epithelial Cells

The glycogen synthase kinase 3β (GSK-3β) is a ubiquitous enzyme that is a validated target for the development of potential therapeutics useful in several diseases including retinal degeneration. Aiming at developing an innovative class of allosteric inhibitors of GSK-3β potentially useful for retinal degeneration, we explored the class of squaramides. The developed compounds (6 a-l) were obtained through a nontoxic one-pot synthetic protocol, which employs low-cost goods and avoids any purification step. Ethanol was used as the reaction solvent, simultaneously allowing the pure reaction products' recovery (by precipitation). Out of this set of squaramides, 6 j stood out, from computational and enzymatic converging data, as an ATP non-competitive inhibitor of GSK-3β of micromolar potency. When engaged in cellular studies using retinal pigment epithelial cells (ARPE-19) transfected with a luciferase reporter gene under the control of T-cell factor/lymphoid enhancer factor (TCF/LEF) binding sites, 6 j was able to dose-dependently induce β-catenin nuclear accumulation, as shown by the increased luciferase activity at a concentration of 2.5 μM.

Resveratrol-like Compounds as SIRT1 Activators

The sirtuin 1 (SIRT1) activator resveratrol has emerged as a promising candidate for the prevention of vascular oxidative stress, which is a trigger for endothelial dysfunction. However, its clinical use is limited by low oral bioavailability. In this work, we have applied a previously developed computational protocol to identify the most promising derivatives from our in-house chemical library of resveratrol derivatives. The most promising compounds in terms of SIRT1 activation and oral bioavailability, predicted in silico, were evaluated for their ability to activate the isolated SIRT1 enzyme. Then, we assessed the antioxidant effects of the most effective derivative, compound 3d, in human umbilical vein endothelial cells (HUVECs) injured with H₂O₂ 100 µM. The SIRT1 activator 3d significantly preserved cell viability and prevented an intracellular reactive oxygen species increase in HUVECs exposed to the oxidative stimulus. Such effects were partially reduced in the presence of a sirtuin inhibitor, sirtinol, confirming the potential role of sirtuins in the activity of resveratrol and its derivatives. Although 3d appeared less effective than resveratrol in activating the isolated enzyme, the effects exhibited by both compounds in HUVECs were almost superimposable, suggesting a higher ability of 3d to cross cell membranes and activate the intracellular target SIRT1.

In silico approach for the development of phenolic derivatives as potential anti-angiogenic agents against lysyl oxidase-like 2 enzyme

Background

Lysyl oxidase-like 2 (LOXL2) has recently been explored as extremely pivotal protein involved in angiogenesis which results in metastasis of numerous types of cancers. Hence, LOXL2 is an exciting new target for drug development against tumor progression and its spread to distant organs. Newly synthesized derivatives of natural phenolic antioxidant guaiacol (T1 to T8) were evaluated for their potential as anti-angiogenic agents using in silico approach. The drug likeness properties and toxicity of the synthesized derivatives have also been determined. Active binding sites of LOXL2 protein were determined by online server DoGSiteScorer, and lead–target interactions and conformations of pose analysis were done by using AutoDock Vina and Discovery Studio 4.0. The GUSAR model was applied to find the toxicity and ADMET properties. On the other hand, the chemoinformatics prediction was also performed using online FAF Drug Server and Molinspiration online server.

Results

Lead molecules from T1 to T8 showed promising binding affinity values, especially T5 and T8 showed best fit in the binding pocket of target enzyme (binding energies − 7.9 and 8.0 kcal/Mol, respectively). The stability of docked complexes was further evaluated using molecular dynamic simulation studies using GROMACS force field, and both leads (T5 and T8) were found to be strongly bounded to the active binding sites. The ADMET results revealed that all experimental molecules were virtually nontoxic and showed compliance with rule of five.

Conclusion

The present work will further enable researchers to understand how computer-aided drug designing tools may help to expedite new drug discovery process in a minimum cost.

Small-Molecule Ebselen Binds to YTHDF Proteins Interfering with the Recognition of N 6-Methyladenosine-Modified RNAs

YTHDF proteins bind the N 6-methyladenosine (m6A)-modified mRNAs, influencing their processing, stability, and translation. Therefore, the members of this protein family play crucial roles in gene regulation and several physiological and pathophysiological conditions. YTHDF proteins contain a hydrophobic pocket that accommodates the m6A embedded in the RRACH consensus sequence on mRNAs. We exploited the presence of this cage to set up an m6A-competitive assay and performed a high-throughput screen aimed at identifying ligands binding in the m6A pocket. We report the organoselenium compound ebselen as the first-in-class inhibitor of the YTHDF m6A-binding domain. Ebselen, whose interaction with YTHDF proteins was validated via orthogonal assays, cannot discriminate between the binding domains of the three YTHDF paralogs but can disrupt the interaction of the YTHDF m6A domain with the m6A-decorated mRNA targets. X-ray, mass spectrometry, and NMR studies indicate that in YTHDF1 ebselen binds close to the m6A cage, covalently to the Cys412 cysteine, or interacts reversibly depending on the reducing environment. We also showed that ebselen engages YTHDF proteins within cells, interfering with their mRNA binding. Finally, we produced a series of ebselen structural analogs that can interact with the YTHDF m6A domain, proving that ebselen expansion is amenable for developing new inhibitors. Our work demonstrates the feasibility of drugging the YTH domain in YTHDF proteins and opens new avenues for the development of disruptors of m6A recognition.

Development of New Potential Inhibitors of β1 Integrins through In Silico Methods-Screening and Computational Validation

Integrins are transmembrane receptors that play a critical role in many biological processes which can be therapeutically modulated using integrin blockers, such as peptidomimetic ligands. This work aimed to develop new potential β1 integrin antagonists using modeled receptors based on the aligned crystallographic structures and docked with three lead compounds (BIO1211, BIO5192, and TCS2314), widely known as α4β1 antagonists. Lead-compound complex optimization was performed by keeping intact the carboxylate moiety of the ligand, adding substituents in two other regions of the molecule to increase the affinity with the target. Additionally, pharmacokinetic predictions were performed for the ten best ligands generated, with the lowest docking interaction energy obtained for α4β1 and BIO5192. Results revealed an essential salt bridge between the BIO5192 carboxylate group and the Mg²⁺ MIDAS ion of the integrin. We then generated more than 200 new BIO5192 derivatives, some with a greater predicted affinity to α4β1. Furthermore, the significance of retaining the pyrrolidine core of the ligand and increasing the therapeutic potential of the new compounds is emphasized. Finally, one novel molecule (1592) was identified as a potential drug candidate, with appropriate pharmacokinetic profiles, similar dynamic behavior at the integrin interaction site compared with BIO5192, and a higher predicted affinity to VLA-4.

Multitarget, Selective Compound Design Yields Potent Inhibitors of a Kinetoplastid Pteridine Reductase 1

The optimization of compounds with multiple targets is a difficult multidimensional problem in the drug discovery cycle. Here, we present a systematic, multidisciplinary approach to the development of selective antiparasitic compounds. Computational fragment-based design of novel pteridine derivatives along with iterations of crystallographic structure determination allowed for the derivation of a structure–activity relationship for multitarget inhibition. The approach yielded compounds showing apparent picomolar inhibition of T. brucei pteridine reductase 1 (PTR1), nanomolar inhibition of L. major PTR1, and selective submicromolar inhibition of parasite dihydrofolate reductase (DHFR) versus human DHFR. Moreover, by combining design for polypharmacology with a property-based on-parasite optimization, we found three compounds that exhibited micromolar EC₅₀ values against T. brucei brucei while retaining their target inhibition. Our results provide a basis for the further development of pteridine-based compounds, and we expect our multitarget approach to be generally applicable to the design and optimization of anti-infective agents.

Transcriptomic changes induced by applications of a commercial extract of Ascophyllum nodosum on tomato plants

Extracts of Ascophyllum nodosum are commonly used as commercial biostimulants in crop production. To further understand the seaweed extract-induced phenomena in plants, a transcriptomic study was conducted. RNA-seq differential gene expression analysis of tomato plants treated with a commercial A. nodosum extract formulation (Stimplex) revealed the up-regulation of 635 and down-regulation of 456 genes. Ontology enrichment analysis showed three gene categories were augmented, including biological processes, cellular components, and molecular functions. KEGG pathway analysis revealed that the extract had a strong influence on the expression of genes involved in carbon fixation, secondary metabolism, MAPK-signalling, plant hormone signal transduction, glutathione metabolism, phenylpropanoid and stilbenoid metabolism, and plant-pathogen interactions. qRT-PCR validation analysis using 15 genes established a strong correlation with the RNA sequencing results. The activities of defence enzymes were also significantly enhanced by seaweed extract treatment. Furthermore, AN-SWE treated tomato plants had significantly higher chlorophyll and growth hormone content and showed improved plant growth parameters and nutrient profiles than the control. It is postulated that seaweed extract-induced gene regulation was responsible for favourable plant responses that enabled better growth and tolerance to stress conditions. This study provides evidence at the transcriptomic level for the positive effects of foliar application of the Ascophyllum nodosum extract (Stimplex) observed in treated tomato plants.

Bioactivity of Novel Pyrazole-Thiazolines Scaffolds against Trypanosoma cruzi: Computational Approaches and 3D Spheroid Model on Drug Discovery for Chagas Disease

Chagas disease, a century-old disease that mainly affects the impoverished population in Latin America, causes high morbidity and mortality in endemic countries. The available drugs, benznidazole (Bz) and nifurtimox, have limited effectiveness and intense side effects. Drug repurposing, and the development of new chemical entities with potent activity against Trypanosoma cruzi, are a potential source of therapeutic options. The present study describes the biological activity of two new series of pyrazole-thiazoline derivatives, based on optimization of a hit system 5-aminopyrazole-imidazoline previously identified, using structure−activity relationship exploration, and computational and phenotype-based strategies. Promising candidates, 2c, 2e, and 2i derivatives, showed good oral bioavailability and ADMET properties, and low cytotoxicity (CC50 > 100 µM) besides potent activity against trypomastigotes (0.4−2.1 µM) compared to Bz (19.6 ± 2.3 µM). Among them, 2c also stands out, with greater potency against intracellular amastigotes (pIC50 = 5.85). The selected pyrazole-thiazoline derivatives showed good permeability and effectiveness in the 3D spheroids system, but did not sustain parasite clearance in a washout assay. The compounds’ mechanism of action is still unknown, since the treatment neither increased reactive oxygen species, nor reduced cysteine protease activity. This new scaffold will be targeted to optimize in order to enhance its biological activity to identify new drug candidates for Chagas disease therapy.

Turning Nonselective Inhibitors of Type I Protein Arginine Methyltransferases into Potent and Selective Inhibitors of Protein Arginine Methyltransferase 4 through a Deconstruction-Reconstruction and Fragment-Growing Approach

Protein arginine methyltransferases (PRMTs) are important therapeutic targets, playing a crucial role in the regulation of many cellular processes and being linked to many diseases. Yet, there is still much to be understood regarding their functions and the biological pathways in which they are involved, as well as on the structural requirements that could drive the development of selective modulators of PRMT activity. Here we report a deconstruction–reconstruction approach that, starting from a series of type I PRMT inhibitors previously identified by us, allowed for the identification of potent and selective inhibitors of PRMT4, which regardless of the low cell permeability show an evident reduction of arginine methylation levels in MCF7 cells and a marked reduction of proliferation. We also report crystal structures with various PRMTs supporting the observed specificity and selectivity.

In Silico Identification of Novel Inhibitors Targeting the Homodimeric Interface of Superoxide Dismutase from the Dental Pathogen Streptococcus mutans

The microaerophile Streptococcus mutans, the main microaerophile responsible for the development of dental plaque, has a single cambialistic superoxide dismutase (SmSOD) for its protection against reactive oxygen species. In order to discover novel inhibitors of SmSOD, possibly interfering with the biofilm formation by this pathogen, a virtual screening study was realised using the available 3D-structure of SmSOD. Among the selected molecules, compound ALS-31 was capable of inhibiting SmSOD with an IC₅₀ value of 159 µM. Its inhibition power was affected by the Fe/Mn ratio in the active site of SmSOD. Furthermore, ALS-31 also inhibited the activity of other SODs. Gel-filtration of SmSOD in the presence of ALS-31 showed that the compound provoked the dissociation of the SmSOD homodimer in two monomers, thus compromising the catalytic activity of the enzyme. A docking model, showing the binding mode of ALS-31 at the dimer interface of SmSOD, is presented. Cell viability of the fibroblast cell line BJ5-ta was not affected up to 100 µM ALS-31. A preliminary lead optimization program allowed the identification of one derivative, ALS-31-9, endowed with a 2.5-fold improved inhibition power. Interestingly, below this concentration, planktonic growth and biofilm formation of S. mutans cultures were inhibited by ALS-31, and even more by its derivative, thus opening the perspective of future drug design studies to fight against dental caries.

Biological Membrane-Penetrating Peptides: Computational Prediction and Applications

Peptides comprise a versatile class of biomolecules that present a unique chemical space with diverse physicochemical and structural properties. Some classes of peptides are able to naturally cross the biological membranes, such as cell membrane and blood-brain barrier (BBB). Cell-penetrating peptides (CPPs) and blood-brain barrier-penetrating peptides (B3PPs) have been explored by the biotechnological and pharmaceutical industries to develop new therapeutic molecules and carrier systems. The computational prediction of peptides’ penetration into biological membranes has been emerged as an interesting strategy due to their high throughput and low-cost screening of large chemical libraries. Structure- and sequence-based information of peptides, as well as atomistic biophysical models, have been explored in computer-assisted discovery strategies to classify and identify new structures with pharmacokinetic properties related to the translocation through biomembranes. Computational strategies to predict the permeability into biomembranes include cheminformatic filters, molecular dynamics simulations, artificial intelligence algorithms, and statistical models, and the choice of the most adequate method depends on the purposes of the computational investigation. Here, we exhibit and discuss some principles and applications of these computational methods widely used to predict the permeability of peptides into biomembranes, exhibiting some of their pharmaceutical and biotechnological applications.

Antioxidant and Anti-Colorectal Cancer Properties in Methanolic Extract of Mangrove-Derived Schizochytrium sp

This work studied the antioxidant and anti-colorectal cancer properties of a potential strain of thraustochytrids, Schizochytrium sp. (SMKK1), isolated from mangrove leaf litter. The biomass was extracted with methanol and screened for antioxidant activity using six different assays. The extract exhibited the highest total antioxidant activity (87.37 ± 1.22%) and the lowest nitric oxide radical (75.12 ± 2.22%), and the activity increased with the concentration of the extract. The methanolic extract was further tested for in vitro cytotoxicity on the colon cancer cell line (HT29). The extract was also analyzed for polyunsaturated fatty acids using GC-MS. The five predominant HTVS-based compounds, viz., arachidonic acid, linolenic acid (alpha-linolenic acid and gamma-linolenic acid), eicosapentaenoic acid, and docosahexaenoic acid, were identified in the extract, and these were tested against the colon cancer protein IGF binding (IGF-1) using the in silico docking method. The results revealed that all the five compounds were capable of destroying the colon oncoprotein responsible for anti-colon carcinogen, based on activation energy and also good hydrogen bond interaction against IGF binding proteins. Of the compounds, docosahexaenoic acid was the most effective, having a docking score of −10.8 Kcal/mol. All the five fatty acids passed the ADMET test and were hence accepted for further clinical trials towards the development of anticancer drugs.

Molecular modeling of natural and synthesized inhibitors against SARS-CoV-2 spike glycoprotein

Purpose

Viral diseases increasingly endanger the world public health because of the transient efficacy of antiviral therapies. The novel coronavirus disease 2019 (COVID-19) has been recently identified as caused by a new type of coronaviruses. This type of coronavirus binds to the human receptor through the Spike glycoprotein (S) Receptor Binding Domain (RBD). The spike protein is found in inaccessible (closed) or accessible (open) conformations in which the accessible conformation causes severe infection. Thus, this receptor is a significant target for antiviral drug design.

Methods

An attempt was made to recognize 111 natural and synthesized compounds in order to utilize them against SARS-CoV-2 spike glycoprotein to inhibit Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) using simulation methods, such as molecular docking. The FAF-Drugs3, Pan-Assay Interference Compounds (PAINS), ADME (absorption, distribution, metabolism, excretion) databases along with Lipinski’s rules were used to evaluate the drug-like properties of the identified ligands. In order to analyze and identify the residues critical in the docking process of the spike glycoprotein, the interactions of proposed ligands with both conformations of the spike glycoprotein was simulated.

Results

The results showed that among the available ligands, seven ligands had significant interactions with the binding site of the spike glycoprotein, in which angiotensin-converting enzyme 2 (ACE2) is bounded. Out of seven candidate molecules, six ligands exhibited drug-like characteristics. The results also demonstrated that fluorophenyl and propane groups of ligands had optimal interactions with the binding site of the spike glycoprotein.

Conclusion

According to the results, our findings indicated the ability of six ligands to prevent the binding of the SARS-CoV-2 spike glycoprotein to its cognate receptor, providing novel compounds for the treatment of COVID-19.

Supplementary Information

The online version contains supplementary material available at 10.1007/s42600-020-00122-3.

Probing Fluorinated Motifs onto Dual AChE-MAO B Inhibitors: Rational Design, Synthesis, Biological Evaluation, and Early-ADME Studies

Bioisosteric H/F or CH₂OH/CF₂H replacement was introduced in coumarin derivatives previously characterized as dual AChE-MAO B inhibitors to probe the effects on both inhibitory potency and drug-likeness. Along with in vitro screening, we investigated early-ADME parameters related to solubility and lipophilicity (Sol_7.4, CHI_7.4, log D_7.4), oral bioavailability and central nervous system (CNS) penetration (PAMPA-HDM and PAMPA-blood-brain barrier (BBB) assays, Caco-2 bidirectional transport study), and metabolic liability (half-lives and clearance in microsomes, inhibition of CYP3A4). Both specific and nonspecific tissue toxicities were determined in SH-SY5Y and HepG2 lines, respectively. Compound 15 bearing a -CF₂H motif emerged as a water-soluble, orally bioavailable CNS-permeant potent inhibitor of both human AChE (IC₅₀ = 550 nM) and MAO B (IC₅₀ = 8.2 nM, B/A selectivity > 1200). Moreover, 15 behaved as a safe and metabolically stable neuroprotective agent, devoid of cytochrome liability.

Identification and Optimization of Novel Small-Molecule Cas9 Inhibitors by Cell-Based High-Throughput Screening

CRISPR/Cas9 has revolutionized several areas of life science; however, methods to control the Cas9 activity are needed for both scientific and therapeutic applications. Anti-CRISPR proteins are known to inhibit the CRISPR/Cas adaptive immunity; however, in vivo delivery of such proteins is problematic. Instead, small-molecule Cas9 inhibitors could serve as useful tools due to their permeable, proteolytically stable, and non-immunogenic nature. Here, we identified a small-molecule ligand with anti-CRISPR/Cas9 activity through a high-throughput screening utilizing an Escherichia coli selection system. Extensive structure-activity relationship studies, which involved a deconstruction-reconstruction strategy, resulted in a range of analogues with significant improvements in the inhibitory activity. Based on NMR and electrophoretic mobility shift assays, we propose that the inhibitory action of these compounds likely results from direct binding to apo-Cas9, preventing Cas9:gRNA complex formation. These molecules may find use as Cas9 modulators in various applications.

Enantioselective Synthesis and Pharmacological Evaluation of Aza-CGP37157–Lipoic Acid Hybrids for the Treatment of Alzheimer’s Disease

Hybrids based on an aza-analogue of CGP37157, a mitochondrial Na⁺/Ca²⁺ exchanger antagonist, and lipoic acid were obtained in order to combine in a single molecule the antioxidant and NRF2 induction properties of lipoic acid and the neuroprotective activity of CGP37157. The four possible enantiomers of the hybrid structure were synthesized by using as the key step a fully diastereoselective reduction induced by Ellman’s chiral auxiliary. After computational druggability studies that predicted good ADME profiles and blood–brain permeation for all compounds, the DPPH assay showed moderate oxidant scavenger capacity. Following a cytotoxicity evaluation that proved the compounds to be non-neurotoxic at the concentrations tested, they were assayed for NRF2 induction capacity and for anti-inflammatory properties and measured by their ability to inhibit nitrite production in the lipopolysaccharide-stimulated BV2 microglial cell model. Moreover, the compounds were studied for their neuroprotective effect in a model of oxidative stress achieved by treatment of SH-SY5Y neuroblastoma cells with the rotenone–oligomycin combination and also in a model of hyperphosphorylation induced by treatment with okadaic acid. The stereocenter configuration showed a critical influence in NRF2 induction properties, and also in the neuroprotection against oxidative stress experiment, leading to the identification of the compound with S and R configuration as an interesting hit with a good neuroprotective profile against oxidative stress and hyperphosphorylation, together with a relevant anti-neuroinflammatory activity. This interesting multitarget profile will be further characterized in future work.

Clinical pharmacology: Current innovations and future challenges

Clinical pharmacology is the study of drugs in humans, from first-in-human studies to randomized controlled trials (RCTs) and benefit-risk ratio assessment in large populations. The objective of this review is to present the recent innovations that may revolutionize the development of drugs in the future. On behalf of the French Society of Pharmacology and Therapeutics, we provide recommendations to address those future challenges in clinical pharmacology. Whatever the future will be, robust preliminary data on drug mechanism of action and rigorous study design will remain crucial prior to the start of pharmacological studies in human. At the present time, RCTs remains the gold standard to evaluate the efficacy of human drugs, although alternative designs (pragmatic trials, platform trials, etc.) are emerging. Innovations in healthy volunteers' studies and the contribution of new technologies such as artificial intelligence, machine learning and internet-based trials have the potential to improve drug development. In the field of precision medicine, new disease phenotypes and endotypes will probably help to identify new pharmacological targets, responders to therapies and patients at risk for drug adverse events. In such a moving landscape, the development of translational research through academic and private partnership, transparent sharing of clinical trial data and enhanced interactions between drug experts, patients and the general public are priority areas for action.

Cysteine derivatives as acetyl lysine mimics to inhibit zinc-dependent histone deacetylases for treating cancer

Zinc-dependent histone deacetylases (HDACs) are important epigenetic regulators that have become important drug targets for treating cancer. Although five HDAC inhibitors have been approved for treating several cancers, there is still a huge demand on discovering new HDAC inhibitors to explore the therapeutic potentials for treating solid tumor cancers. Substrate mimics are a powerful rational design approach for the development of potent inhibitors. Here we describe the rational design, synthesis, biological evaluation, molecular docking and in vivo efficacy study of a class of HDAC inhibitors using N^ε-acetyl lysine mimics that are derived from cysteine. As a result, compounds 7a, 9b and 13d demonstrated pan-HDAC inhibition and broad cytotoxicity against several cancer cell lines, comparable to the approved HDAC inhibitor SAHA. Furthermore, 13d significantly inhibited tumor growth in a A549 xenograft mice model without any obvious weight loss, supporting that the cysteine-derived acetyl lysine mimics are promising HDAC inhibitors with therapeutic potentials for treating cancer.

Structural Optimization and Biological Activity of Pyrazole Derivatives: Virtual Computational Analysis, Recovery Assay and 3D Culture Model as Potential Predictive Tools of Effectiveness against Trypanosoma cruzi

Chagas disease, a chronic and silent disease caused by Trypanosoma cruzi, is currently a global public health problem. The treatment of this neglected disease relies on benznidazole and nifurtimox, two nitroheterocyclic drugs that show limited efficacy and severe side effects. The failure of potential drug candidates in Chagas disease clinical trials highlighted the urgent need to identify new effective chemical entities and more predictive tools to improve translational success in the drug development pipeline. In this study, we designed a small library of pyrazole derivatives (44 analogs) based on a hit compound, previously identified as a T. cruzi cysteine protease inhibitor. The in vitro phenotypic screening revealed compounds 3g, 3j, and 3m as promising candidates, with IC₅₀ values of 6.09 ± 0.52, 2.75 ± 0.62, and 3.58 ± 0.25 µM, respectively, against intracellular amastigotes. All pyrazole derivatives have good oral bioavailability prediction. The structure–activity relationship (SAR) analysis revealed increased potency of 1-aryl-1H-pyrazole-imidazoline derivatives with the Br, Cl, and methyl substituents in the para-position. The 3m compound stands out for its trypanocidal efficacy in 3D microtissue, which mimics tissue microarchitecture and physiology, and abolishment of parasite recrudescence in vitro. Our findings encourage the progression of the promising candidate for preclinical in vivo studies.

Natural Products-Based Drug Design against SARS-CoV-2 Mpro 3CLpro

Coronavirus disease 2019 (COVID-19), caused by severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), has received global attention due to the serious threat it poses to public health. Since the outbreak in December 2019, millions of people have been affected and its rapid global spread has led to an upsurge in the search for treatment. To discover hit compounds that can be used alone or in combination with repositioned drugs, we first analyzed the pharmacokinetic and toxicological properties of natural products from Brazil's semiarid region. After, we analyzed the site prediction and druggability of the SARS-CoV-2 main protease (Mpro), followed by docking and molecular dynamics simulation. The best SARS-CoV-2 Mpro complexes revealed that other sites were accessed, confirming that our approach could be employed as a suitable starting protocol for ligand prioritization, reinforcing the importance of catalytic cysteine-histidine residues and providing new structural data that could increase the antiviral development mainly against SARS-CoV-2. Here, we selected 10 molecules that could be in vitro assayed in response to COVID-19. Two compounds (b01 and b02) suggest a better potential for interaction with SARS-CoV-2 Mpro and could be further studied.

Identification of novel SIRT1 activators endowed with cardioprotective profile

Drugs targeting epigenetic mechanisms are attracting the attention of scientists since it was observed that the modulation of this post-translational apparatus, could help to identify innovative therapeutic strategies. Among the epigenetic druggable targets, the positive modulation of SIRT1 has also been related to significant cardioprotective effects. Unfortunately, actual SIRT1 activators (natural products and synthetic molecules) suffer from several drawbacks, particularly poor pharmacokinetic profiles. Accordingly, in this article we present the development of an integrated screening platform aimed at identifying novel SIRT1 activators with favorable drug-like features as cardioprotective agents. Encompassing several competencies (in silico, medicinal chemistry, and pharmacology), we describe a multidisciplinary approach for rapidly identifying SIRT1 activators and their preliminary pharmacological characterization. In the first step, we virtually screened an in-house chemical library comprising synthetic molecules inspired by nature, against SIRT1 enzyme. To this end, we combined molecular docking-based approach with the estimation of relative ligand binding energy, using the crystal structure of SIRT1 enzyme in complex with resveratrol. Eleven computational hits were identified, synthesized and tested against the isolated enzyme for validating the in silico strategy. Among the tested molecules, five of them behave as SIRT1 enzyme activators. Due to the superior response in activating the enzyme and its favorable calculated physico-chemical properties, compound 8 was further characterized in ex vivo studies on isolated and perfused rat hearts submitted to ischemia/reperfusion (I/R) period. The pharmacological profile of compound 8, suggests that this molecule represents a prototypic SIRT1 activator with satisfactory drug-like profile, paving the way for developing novel epigenetic cardioprotective agents.

Modulators of immunoregulatory exonucleases PLD3 and PLD4 identified by high-throughput screen

PLD3 and PLD4 have recently been revealed to be endosomal exonucleases that regulate the innate immune response by digesting the ligands of nucleic acid sensors. These enzymes can suppress RNA and DNA innate immune sensors like toll-like receptor 9, and PLD4-deficent mice exhibit inflammatory disease. Targeting these immunoregulatory enzymes presents an opportunity to indirectly regulate innate immune nucleic acid sensors that could yield immunotherapies, adjuvants, and nucleic acid drug stabilizers. To aid in delineating the therapeutic potential of these targets, we have developed a high-throughput fluorescence enzymatic assay to identify modulators of PLD3 and PLD4. Screening of a diversity library (N = 17952) yielded preferential inhibitors of PLD3 and PLD4 in addition to a PLD3 selective activator. The modulation models of these compounds were delineated by kinetic analysis. This work presents an inexpensive and simple method to identify modulators of these immunoregulatory exonucleases.

Addressing Compound Reactivity and Aggregation Assay Interferences: Case Studies of Biochemical High-Throughput Screening Campaigns Benefiting from the National Institutes of Health Assay Guidance Manual Guidelines

Compound-dependent assay interferences represent a continued burden in drug and chemical probe discovery. The open-source National Institutes of Health/National Center for Advancing Translational Sciences (NIH/NCATS) Assay Guidance Manual (AGM) established an "Assay Artifacts and Interferences" section to address different sources of artifacts and interferences in biological assays. In addition to the frequent introduction of new chapters in this important topic area, older chapters are periodically updated by experts from academia, industry, and government to include new technologies and practices. Section chapters describe many best practices for mitigating and identifying compound-dependent assay interferences. Using two previously reported biochemical high-throughput screening campaigns for small-molecule inhibitors of the epigenetic targets Rtt109 and NSD2, the authors review best practices and direct readers to high-yield resources in the AGM and elsewhere for the mitigation and identification of compound-dependent reactivity and aggregation assay interferences.

Molecular insight into isoform specific inhibition of PI3K-α and PKC-η with dietary agents through an ensemble pharmacophore and docking studies

Dietary compounds play an important role in the prevention and treatment of many cancers, although their specific molecular mechanism is not yet known. In the present study, thirty dietary agents were analyzed on nine drug targets through in silico studies. However, nine dietary scaffolds, such as silibinin, flavopiridol, oleandrin, ursolic acid, α-boswellic acid, β-boswellic acid, triterpenoid, guggulsterone, and oleanolic acid potentially bound to the cavity of PI3K-α, PKC-η, H-Ras, and Ras with the highest binding energy. Particularly, the compounds silibinin and flavopiridol have been shown to have broad spectrum anticancer activity. Interestingly, flavopiridol was embedded in the pockets of PI3K-α and PKC-η as bound crystal inhibitors in two different conformations and showed significant interactions with ATP binding pocket residues. However, complex-based pharmacophore modeling achieved two vital pharmacophoric features namely, two H-bond acceptors for PI3K-α, while three are hydrophobic, one cat-donor and one H-bond donor and acceptor for PKC-η, respectively. The database screening with the ChemBridge core library explored potential hits on a valid pharmacophore query. Therefore, to optimize perspective lead compounds from the hits, which were subjected to various constraints such as docking, MM/GBVI, Lipinski rule of five, ADMET and toxicity properties. Henceforth, the top ligands were sorted out and examined for vital interactions with key residues, arguably the top three promising lead compounds for PI3K-α, while seven for PKC-η, exhibiting binding energy from - 11.5 to - 8.5 kcal mol^-1. Therefore, these scaffolds could be helpful in the development of novel class of effective anticancer agents.

Applications of Virtual Screening in Bioprospecting: Facts, Shifts, and Perspectives to Explore the Chemo-Structural Diversity of Natural Products

Natural products are continually explored in the development of new bioactive compounds with industrial applications, attracting the attention of scientific research efforts due to their pharmacophore-like structures, pharmacokinetic properties, and unique chemical space. The systematic search for natural sources to obtain valuable molecules to develop products with commercial value and industrial purposes remains the most challenging task in bioprospecting. Virtual screening strategies have innovated the discovery of novel bioactive molecules assessing in silico large compound libraries, favoring the analysis of their chemical space, pharmacodynamics, and their pharmacokinetic properties, thus leading to the reduction of financial efforts, infrastructure, and time involved in the process of discovering new chemical entities. Herein, we discuss the computational approaches and methods developed to explore the chemo-structural diversity of natural products, focusing on the main paradigms involved in the discovery and screening of bioactive compounds from natural sources, placing particular emphasis on artificial intelligence, cheminformatics methods, and big data analyses.

Predicting cell-penetrating peptides using machine learning algorithms and navigating in their chemical space

Cell-penetrating peptides (CPPs) are naturally able to cross the lipid bilayer membrane that protects cells. These peptides share common structural and physicochemical properties and show different pharmaceutical applications, among which drug delivery is the most important. Due to their ability to cross the membranes by pulling high-molecular-weight polar molecules, they are termed Trojan horses. In this study, we proposed a machine learning (ML)-based framework named BChemRF-CPPred (beyond chemical rules-based framework for CPP prediction) that uses an artificial neural network, a support vector machine, and a Gaussian process classifier to differentiate CPPs from non-CPPs, using structure- and sequence-based descriptors extracted from PDB and FASTA formats. The performance of our algorithm was evaluated by tenfold cross-validation and compared with those of previously reported prediction tools using an independent dataset. The BChemRF-CPPred satisfactorily identified CPP-like structures using natural and synthetic modified peptide libraries and also obtained better performance than those of previously reported ML-based algorithms, reaching the independent test accuracy of 90.66% (AUC = 0.9365) for PDB, and an accuracy of 86.5% (AUC = 0.9216) for FASTA input. Moreover, our analyses of the CPP chemical space demonstrated that these peptides break some molecular rules related to the prediction of permeability of therapeutic molecules in cell membranes. This is the first comprehensive analysis to predict synthetic and natural CPP structures and to evaluate their chemical space using an ML-based framework. Our algorithm is freely available for academic use at http://comptools.linc.ufpa.br/BChemRF-CPPred .

The identification of novel inhibitors of human angiotensin-converting enzyme 2 and main protease of Sars-Cov-2: A combination of in silico methods for treatment of COVID-19

The angiotensin-converting enzyme 2 (ACE2) and main protease (MPro), are the putative drug candidates for the severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2). In this study, we performed 3D-QSAR pharmacophore modeling and screened 1,264,479 ligands gathered from Pubchem and Zinc databases. Following the calculation of the ADMET properties, molecular docking was carried out. Moreover, the de novo ligand design was performed. MD simulation was then applied to survey the behavior of the ligand-protein complexes. Furthermore, MMPBSA has utilized to re-estimate the binding affinities. Then, a free energy landscape was used to find the most stable conformation of the complexes. Finally, the hybrid QM-MM method was carried out for the precise calculation of the energies.

The Hypo1 pharmacophore model was selected as the best model. Our docking results indicate that the compounds ZINC12562757 and 112,260,215 were the best potential inhibitors of the ACE2 and MPro, respectively. Furthermore, the Evo_1 compound enjoys the highest docking energy among the designed de novo ligands. Results of RMSD, RMSF, H-bond, and DSSP analyses have demonstrated that the lead compounds preserve the stability of the complexes’ conformation during the MD simulation. MMPBSA data confirmed the molecular docking results. The results of QM-MM showed that Evo_1 has a stronger potential for specific inhibition of MPro, as compared to the 112,260,215 compound.

QSAR Models for Active Substances against Pseudomonas aeruginosa Using Disk-Diffusion Test Data

Pseudomonas aeruginosa is a Gram-negative bacillus included among the six “ESKAPE” microbial species with an outstanding ability to “escape” currently used antibiotics and developing new antibiotics against it is of the highest priority. Whereas minimum inhibitory concentration (MIC) values against Pseudomonas aeruginosa have been used previously for QSAR model development, disk diffusion results (inhibition zones) have not been apparently used for this purpose in the literature and we decided to explore their use in this sense. We developed multiple QSAR methods using several machine learning algorithms (support vector classifier, K nearest neighbors, random forest classifier, decision tree classifier, AdaBoost classifier, logistic regression and naïve Bayes classifier). We used four sets of molecular descriptors and fingerprints and three different methods of data balancing, together with the “native” data set. In total, 32 models were built for each set of descriptors or fingerprint and balancing method, of which 28 were selected and stacked to create meta-models. In terms of balanced accuracy, the best performance was provided by KNN, logistic regression and decision tree classifier, but the ensemble method had slightly superior results in nested cross-validation.

In silico analyses of toxicity of the major constituents of essential oils from two Ipomoea L. species

Convolvulaceae Juss. is a family of vines and shrubs composed of species of ecological and economic importance. Ipomoea asarifolia (Desr.) Roem. & Schult. and I. setifera Poir. are ruderal and evergreen weeds that invade pastures and cause intoxication in cattle during the dry season. In the present study, the essential oils (EOs) of the leaves from I. setifera (dry season) and I. asarifolia (dry and wet seasons) were obtained by steam distillation for 3h. The chemical composition of the EOs was determined using gas chromatography coupled to gas spectrometry (CG/MS) and gas chromatography with flame ionization detector (CG-FID). To correlate the toxicity of the major chemical constituents of I. setifera and I. asarifolia EOs, we predicted the inhibition activity against the cytochrome P450 (CYP450) and P-glycoprotein 1 (P-gp) using a machine learning-based (ML-based) algorithm. In silico analyses were also applied to evaluate the pharmacokinetics properties related to the penetration in the blood-brain barrier (BBB) and gastrointestinal absorption. The chemical composition of the EO of I. setifera was characterized by high levels of (E)-caryophyllene (36.7%) and β-elemene (20.49%). The I. asarifolia EO showed a phytol derivative as the main chemical constituent in the dry season (35.49%), and its content was reduced in the sample collected during the wet season (10.67%). The constituent (E)-caryophyllene was also present in the leaves of I. asarifolia, but at lower levels (15.93-19.93%) when compared to the EOs of I. setifera. Our computational analyses indicated that the constituents caryophyllene oxide, cedroxyde, pentadecanal, and phytol can be related to the toxicity of these weeds. This is the first study to report the chemical composition of I. asarifolia and I. setifera EOs and correlate their molecular mechanism of toxicity using in silico approaches.

Discovery of New Catalytic Topoisomerase II Inhibitors for Anticancer Therapeutics

Poison inhibitors of DNA topoisomerase II (TOP2) are clinically used drugs that cause cancer cell death by inducing DNA damage, which mechanism of action is also associated with serious side effects such as secondary malignancy and cardiotoxicity. In contrast, TOP2 catalytic inhibitors induce limited DNA damage, have low cytotoxicity, and are effective in suppressing cancer cell proliferation. They have been sought after to be prospective anticancer therapies. Herein the discovery of new TOP2 catalytic inhibitors is described. A new druggable pocket of TOP2 protein at its DNA binding domain was used as a docking site to virtually screen ~6 million molecules from the ZINC15 library. The lead compound, T60, was characterized to be a catalytic TOP2 inhibitor that binds TOP2 protein and disrupts TOP2 from interacting with DNA, resulting in no DNA cleavage. It has low cytotoxicity, but strongly inhibits cancer cell proliferation and xenograft growth. T60 also inhibits androgen receptor activity and prostate cancer cell growth. These results indicate that T60 is a promising candidate compound that can be further developed into new anticancer drugs.

VSPrep: A KNIME Workflow for the Preparation of Molecular Databases for Virtual Screening

Drug discovery is a challenging and expensive field. Hence, novel in silico tools have been developed in early discovery stage to identify and prioritize novel molecules with suitable physicochemical properties. In many in silico drug design projects, molecular databases are screened by virtual screening tools to search for potential bioactive molecules. The preparation of the molecules is therefore a key step in the success of well-established techniques such as docking, similarity or pharmacophore searching. We review here the lists of several toolkits used in different steps during the cleaning of molecular databases, integrated within a KNIME workflow. During the first step of the automatic workflow, salts are removed, and mixtures are split to get one compound per entry. Then compounds with unwanted features are filtered. Duplicated entries are then deleted while considering stereochemistry. As a compromise between exhaustiveness and computational time, most distributed tautomers at physiological pH are computed. Additionally, various flags are applied to molecules by using either classical molecular descriptors, similarity search to known libraries or substructure search rules. Moreover, stereoisomers are enumerated depending on the unassigned chiral centers. Then, three-dimensional coordinates, and optionally conformers, are generated. This workflow has been already applied to several drug design projects and can be used for molecular database preparation upon request.