Phantom PAINS: Problems with the Utility of Alerts for Pan-Assay INterference CompoundS

ChemFH: an integrated tool for screening frequent false positives in chemical biology and drug discovery

High-throughput screening rapidly tests an extensive array of chemical compounds to identify hit compounds for specific biological targets in drug discovery. However, false-positive results disrupt hit compound screening, leading to wastage of time and resources. To address this, we propose ChemFH, an integrated online platform facilitating rapid virtual evaluation of potential false positives, including colloidal aggregators, spectroscopic interference compounds, firefly luciferase inhibitors, chemical reactive compounds, promiscuous compounds, and other assay interferences. By leveraging a dataset containing 823 391 compounds, we constructed high-quality prediction models using multi-task directed message-passing network (DMPNN) architectures combining uncertainty estimation, yielding an average AUC value of 0.91. Furthermore, ChemFH incorporated 1441 representative alert substructures derived from the collected data and ten commonly used frequent hitter screening rules. ChemFH was validated with an external set of 75 compounds. Subsequently, the virtual screening capability of ChemFH was successfully confirmed through its application to five virtual screening libraries. Furthermore, ChemFH underwent additional validation on two natural products and FDA-approved drugs, yielding reliable and accurate results. ChemFH is a comprehensive, reliable, and computationally efficient screening pipeline that facilitates the identification of true positive results in assays, contributing to enhanced efficiency and success rates in drug discovery. ChemFH is freely available via https://chemfh.scbdd.com/.

Luteolin as a potential therapeutic candidate for lung cancer: Emerging preclinical evidence

Lung cancer is a prevalent malignant tumor and a leading cause of cancer-related fatalities globally. However, current treatments all have limitations. Therefore, there is an urgent need to identify a readily available therapeutic agent to counteract lung cancer development and progression. Luteolin is a flavonoid derived from vegetables and herbs that possesses preventive and therapeutic effects on various cancers. With the goal of providing new directions for the treatment of lung cancer, we review here the recent findings on luteolin so as to provide new ideas for the development of new anti-lung cancer drugs. The search focused on studies published between January 1995 and January 2024 that explored the use of luteolin in lung cancer. A comprehensive literature search was conducted in the SCOPUS, Google Scholar, PubMed, and Web of Science databases using the keywords "luteolin" and "lung cancer." By collecting previous literature, we found that luteolin has multiple mechanisms of therapeutic effects, including promotion of apoptosis in lung cancer cells; inhibition of tumor cell proliferation, invasion and metastasis; and modulation of immune responses. In addition, it can be used as an adjuvant to radio-chemotherapy and helps to ameliorate cancer complications. This review summarizes the structure, natural sources, physicochemical properties and pharmacokinetics of luteolin, and focuses on the anti-lung cancer mechanism of luteolin, so as to provide new ideas for the development of new anti-lung cancer drugs.

Carltonine-derived compounds for targeted butyrylcholinesterase inhibition

The investigation into human butyrylcholinesterase (hBChE) inhibitors as therapeutic agents for Alzheimer's disease (AD) holds significant promise, addressing both symptomatic relief and disease progression. In the pursuit of novel drug candidates with a selective BChE inhibition pattern, we focused on naturally occurring template structures, specifically Amaryllidaceae alkaloids of the carltonine-type. Herein, we explored a series of compounds implementing an innovative chemical scaffold built on the 3- and 4-benzyloxy-benzylamino chemotype. Notably, compounds 28 (hBChE IC50 = 0.171 ± 0.063 μM) and 33 (hBChE IC50 = 0.167 ± 0.018 μM) emerged as top-ranked hBChE inhibitors. In silico simulations elucidated the binding modes of these compounds within hBChE. CNS availability was predicted using the BBB score algorithm, corroborated by in vitro permeability assessments with the most potent derivatives. Compound 33 was also inspected for aqueous solubility, microsomal and plasma stability. Chemoinformatics analysis validated these hBChE inhibitors for oral administration, indicating favorable gastrointestinal absorption in compliance with Lipinski's and Veber's rules. Safety assessments, crucial for the chronic administration typical in AD treatment, were conducted through cytotoxicity testing on human neuroblastoma (SH-SY5Y) and hepatocellular carcinoma (HepG2) cell lines.

Exploring SureChEMBL from a drug discovery perspective

In the pharmaceutical industry, the patent protection of drugs and medicines is accorded importance because of the high costs involved in the development of novel drugs. Over the years, researchers have analyzed patent documents to identify freedom-to-operate spaces for novel drug candidates. To assist this, several well-established public patent document data repositories have enabled automated methodologies for extracting information on therapeutic agents. In this study, we delve into one such publicly available patent database, SureChEMBL, which catalogues patent documents related to life sciences. Our exploration begins by identifying patent compounds across public chemical data resources, followed by pinpointing sections in patent documents where the chemical annotations were found. Next, we exhibit the potential of compounds to serve as drug candidates by evaluating their conformity to drug-likeness criteria. Lastly, we examine the drug development stage reported for these compounds to understand their clinical success. In summary, our investigation aims at providing a comprehensive overview of the patent compounds catalogued in SureChEMBL, assessing their relevance to pharmaceutical drug discovery.

Investigating the antiviral therapeutic potentialities of marine polycyclic lamellarin pyrrole alkaloids as promising inhibitors for SARS-CoV-2 and Zika main proteases (Mpro)

The new coronavirus variant (SARS-CoV-2) and Zika virus are two world-wide health pandemics. Along history, natural products-based drugs have always crucially recognized as a main source of valuable medications. Considering the SARS-CoV-2 and Zika main proteases (Mpro) as the re-production key element of the viral cycle and its main target, herein we report an intensive computer-aided virtual screening for a focused list of 39 marine lamellarins pyrrole alkaloids, against SARS-CoV-2 and Zika main proteases (Mpro) using a set of combined modern computational methodologies including molecular docking (MDock), molecule dynamic simulations (MDS) and structure-activity relationships (SARs) as well. Indeed, the molecular docking studies had revealed four promising marine alkaloids including [lamellarin H (14)/K (17)] and [lamellarin S (26)/Z (39)], according to their notable ligand-protein energy scores and relevant binding affinities with the SARS-CoV-2 and Zika (Mpro) pocket residues, respectively. Consequentially, these four chemical hits were further examined thermodynamically though investigating their MD simulations at 100 ns, where they showed prominent stability within the accommodated (Mpro) pockets. Moreover, in-deep SARs studies suggested the crucial roles of the rigid fused polycyclic ring system, particularly aromatic A- and F- rings, position of the phenolic -OH and δ-lactone functionalities as essential structural and pharmacophoric features. Finally, these four promising lamellarins alkaloids were investigated for their in-silico ADME using the SWISS ADME platform, where they displayed appropriated drug-likeness properties. Such motivating outcomes are greatly recommending further in vitro/vivo examinations regarding those lamellarins pyrrole alkaloids (LPAs).Communicated by Ramaswamy H. Sarma.

Tackling assay interference associated with small molecules

Biochemical and cell-based assays are essential to discovering and optimizing efficacious and safe drugs, agrochemicals and cosmetics. However, false assay readouts stemming from colloidal aggregation, chemical reactivity, chelation, light signal attenuation and emission, membrane disruption, and other interference mechanisms remain a considerable challenge in screening synthetic compounds and natural products. To address assay interference, a range of powerful experimental approaches are available and in silico methods are now gaining traction. This Review begins with an overview of the scope and limitations of experimental approaches for tackling assay interference. It then focuses on theoretical methods, discusses strategies for their integration with experimental approaches, and provides recommendations for best practices. The Review closes with a summary of the critical facts and an outlook on potential future developments.

Identification and Biological Evaluation of a Novel Small-Molecule Inhibitor of Ricin Toxin

The plant-derived toxin ricin is classified as a type 2 ribosome-inactivating protein (RIP) and currently lacks effective clinical antidotes. The toxicity of ricin is mainly due to its ricin toxin A chain (RTA), which has become an important target for drug development. Previous studies have identified two essential binding pockets in the active site of RTA, but most existing inhibitors only target one of these pockets. In this study, we used computer-aided virtual screening to identify a compound called RSMI-29, which potentially interacts with both active pockets of RTA. We found that RSMI-29 can directly bind to RTA and effectively attenuate protein synthesis inhibition and rRNA depurination induced by RTA or ricin, thereby inhibiting their cytotoxic effects on cells in vitro. Moreover, RSMI-29 significantly reduced ricin-mediated damage to the liver, spleen, intestine, and lungs in mice, demonstrating its detoxification effect against ricin in vivo. RSMI-29 also exhibited excellent drug-like properties, featuring a typical structural moiety of known sulfonamides and barbiturates. These findings suggest that RSMI-29 is a novel small-molecule inhibitor that specifically targets ricin toxin A chain, providing a potential therapeutic option for ricin intoxication.

Discovery of New Tricyclic Oxime Sampangine Derivatives as Potent Antifungal Agents for the Treatment of Cryptococcosis and Candidiasis

Cryptococcus neoformans (C. neoformans) and Candida albicans (C. albicans) are classified as the critical priority groups among the pathogenic fungi, highlighting the urgent need for developing more effective antifungal therapies. On the basis of antifungal natural product sampangine, herein, a series of tricyclic oxime and oxime ether derivatives were designed. Among them, compound WZ-2 showed excellent inhibitory activity against C. neoformans (MIC80 = 0.016 μg/mL) and synergized with fluconazole to treat resistant C. albicans (FICI = 0.078). Interestingly, compound WZ-2 effectively inhibited virulence factors (e.g., capsule, biofilm, and yeast-to-hypha morphological transition), suggesting the potential to overcome drug resistance. In a mouse model of cryptococcal meningitis, compound WZ-2 (5 mg/kg) effectively reduced the brain C. neoformans H99 burden. Furthermore, compound WZ-2 alone and its combination with fluconazole also significantly reduced the kidney burden of the drug-resistant strain (0304103) and sensitive strain (SC5314) of C. albicans.

Navigating the Chemical Space of ENR Inhibitors: A Comprehensive Analysis

Antimicrobial resistance is a global health threat that requires innovative strategies against drug-resistant bacteria. Our study focuses on enoyl-acyl carrier protein reductases (ENRs), in particular FabI, FabK, FabV, and InhA, as potential antimicrobial agents. Despite their promising potential, the lack of clinical approvals for inhibitors such as triclosan and isoniazid underscores the challenges in achieving preclinical success. In our study, we curated and analyzed a dataset of 1412 small molecules recognized as ENR inhibitors, investigating different structural variants. Using advanced cheminformatic tools, we mapped the physicochemical landscape and identified specific structural features as key determinants of bioactivity. Furthermore, we investigated whether the compounds conform to Lipinski rules, PAINS, and Brenk filters, which are crucial for the advancement of compounds in development pipelines. Furthermore, we investigated structural diversity using four different representations: Chemotype diversity, molecular similarity, t-SNE visualization, molecular complexity, and cluster analysis. By using advanced bioinformatics tools such as matched molecular pairs (MMP) analysis, machine learning, and SHAP analysis, we were able to improve our understanding of the activity cliques and the precise effects of the functional groups. In summary, this chemoinformatic investigation has unraveled the FAB inhibitors and provided insights into rational antimicrobial design, seamlessly integrating computation into the discovery of new antimicrobial agents.

Curation and cheminformatics analysis of a Ugi-reaction derived library (URDL) of synthetically tractable small molecules for virtual screening application

Virtual screening (VS) is an important approach in drug discovery and relies on the availability of a virtual library of synthetically tractable molecules. Ugi reaction (UR) represents an important multi-component reaction (MCR) that reliably produces a peptidomimetic scaffold. Recent literature shows that a tactically assembled Ugi adduct can be subjected to further chemical modifications to yield a variety of rings and scaffolds, thus, renewing the interest in this old reaction. Given the reliability and efficiency of UR, we collated an UR derived library (URDL) of small molecules (total = 5773) for VS. The synthesis of the majority of URDL molecules may be carried out in 1-2 pots in a time and cost-effective manner. The detailed analysis of the average property and chemical space of URDL was also carried out using the open-source Datawarrior program. The comparison with FDA-approved oral drugs and inhibitors of protein-protein interactions (iPPIs) suggests URDL molecules are 'clean', drug-like, and conform to a structurally distinct space from the other two categories. The average physicochemical properties of compounds in the URDL library lie closer to iPPI molecules than oral drugs thus suggesting that the URDL resource can be applied to discover novel iPPI molecules. The URDL molecules consist of diverse ring systems, many of which have not been exploited yet for drug design. Thus, URDL represents a small virtual library of drug-like molecules with unexplored chemical space designed for VS. The structures of all molecules of URDL, oral drugs, and iPPI compounds are being made freely accessible as supplementary information for broader application.

Identification and characterization of a novel decalin derivative with anti-Candida activity from Streptomyces chrestomyceticus strain ADP4

A novel decalin derivative, trans-1-oxo-2,4-diacetylaminodecalin (1) with anti-Candida activity, had been isolated from Streptomyces chrestomyceticus strain ADP4. The structure of the compound was determined from the analysis of spectral data (LCMS/MS, UV, FTIR, 1D- and 2D-NMR). The anti-Candida activity of 1 was specific to Candida albicans and Candida auris. Further, it displayed inhibition of the early-stage biofilm of C. albicans. In-silico analysis of the compound revealed its drug likeness properties without any violations and PAINS alert when investigated for ADME properties. Along with the overall bioavailability, compound 1 did not show any predicted bioaccumulation and mutagenicity in the analysis by TEST software. Non-cytotoxic property was further confirmed by in-vitro assay on the HepG2 cell line.

Design and Diversity Analysis of Chemical Libraries in Drug Discovery

Chemical libraries and compound data sets are among the main inputs to start the drug discovery process at universities, research institutes, and the pharmaceutical industry. The approach used in the design of compound libraries, the chemical information they possess, and the representation of structures, play a fundamental role in the development of studies: chemoinformatics, food informatics, in silico pharmacokinetics, computational toxicology, bioinformatics, and molecular modeling to generate computational hits that will continue the optimization process of drug candidates. The prospects for growth in drug discovery and development processes in chemical, biotechnological, and pharmaceutical companies began a few years ago by integrating computational tools with artificial intelligence methodologies. It is anticipated that it will increase the number of drugs approved by regulatory agencies shortly.

Karenia brevis Extract Induces Cellular Entry through Distinct Mechanisms in Phagocytic RAW 264.7 Macrophages versus Non-Phagocytic Vero Cells

Marine algae extracts are an important area of potential drug discovery; however, nearly all studies to date have used non-fluorescent-based methods to determine changes in target cell activity. Many of the most robust immunological and cellular analyses rely on fluorescent probes and readouts, which can be problematic when the algae extract is fluorescent itself. In this study, we identified the fluorescent spectrum of an isolated extract from the marine dinoflagellate Karenia brevis, which included two fluorescing components: chlorophyll α and pheophytin α. When excited at 405 nm and 664 nm, the extract emitted fluorescence at 676 nm and 696 nm, respectively. The extract and its fluorescing components, chlorophyll α and pheophytin α, entered phagocytic RAW 264.7 macrophages and non-phagocytic Vero kidney cells through distinct mechanisms. When incubated with the extract and its main components, both the RAW 264.7 macrophages and the Vero cells accumulated fluorescence as early as 30 min and continued through 48 h. Vero kidney cells accumulated the K. brevis fluorescent extract through a dynamin-independent and acidified endosomal-dependent mechanism. RAW 264.7 macrophages accumulated fluorescent extract through a dynamin-independent, acidified endosomal-independent mechanism, which supports accumulation through phagocytosis. Furthermore, RAW 264.7 macrophages downregulated cell-surface expression of CD206 in response to extract stimulation indicating activation of phagocytic responses and potential immunosuppression of these immune cells. This study represents the first characterization of the cellular update of K. brevis extracts in phagocytic versus non-phagocytic cells. The data suggest the importance of understanding cellular uptake of fluorescing algae extracts and their mechanism of action for future drug discovery efforts.

Echinacoside, a promising sortase A inhibitor, combined with vancomycin against murine models of MRSA-induced pneumonia

Methicillin-resistant Staphylococcus aureus (MRSA) is a pathogenic bacterium responsible for a range of severe infections, such as skin infections, bacteremia, and pneumonia. Due to its antibiotic-resistant nature, current research focuses on targeting its virulence factors. Sortase A (SrtA) is a transpeptidase that anchors surface proteins to the bacterial cell wall and is involved in adhesion and invasion to host cells. Through fluorescence resonance energy transfer (FRET), we identified echinacoside (ECH), a natural polyphenol, as a potential SrtA inhibitor with an IC50 of 38.42 μM in vitro. It was demonstrated that ECH inhibited SrtA-mediated S. aureus fibrinogen binding, surface protein A anchoring, and biofilm formation. The fluorescence quenching assay determined the binding mode of ECH to SrtA and calculated the KA-binding constant of 3.09 × 105 L/mol, demonstrating the direct interaction between the two molecules. Molecular dynamics simulations revealed that ECH-SrtA interactions occurred primarily at the binding sites of A92G, A104G, V168A, G192A, and R197A. Importantly, the combination of ECH and vancomycin offered protection against murine models of MRSA-induced pneumonia. Therefore, ECH may serve as a potential antivirulence agent against S. aureus infections, either alone or in combination with vancomycin.

Fused Tetrahydroquinolines Are Interfering with Your Assay

Tricyclic tetrahydroquinolines (THQs) have been repeatedly reported as hits across a diverse range of high-throughput screening (HTS) campaigns. The activities of these compounds, however, are likely due to reactive byproducts that interfere with the assay. As a lesser studied class of pan-assay interference compounds, the mechanism by which fused THQs react with protein targets remains largely unknown. During HTS follow-up, we characterized the behavior and stability of several fused tricyclic THQs. We synthesized key analogues to pinpoint the cyclopentene ring double bond as a source of reactivity of fused THQs. We found that these compounds degrade in solution under standard laboratory conditions in days. Importantly, these observations make it likely that fused THQs, which are ubiquitously found within small molecule screening libraries, are unlikely the intact parent compounds. We urge deprioritization of tricylic THQ hits in HTS follow-up and caution against the investment of resources to follow-up on these problematic compounds.

Interception of Epoxide ring to quorum sensing system in Enterococcus faecalis and Staphylococcus aureus

Quorum sensing inhibitor (QSI) has been attracting attention as anti-virulence agent which disarms pathogens of their virulence rather than killing them. QSI marking cyclic peptide-mediated QS in Gram-positive bacteria is an effective tool to overcome the crisis of antibiotic-dependent chemotherapy due to the emergence of drug resistance strain, e.g., methicillin resistant Staphylococcus aureus (MRSA) and Vancomycin resistant Enterococci (VRE). From a semi-large-scale screening thus far carried out, two Epoxide compounds, Ambuic acid and Synerazol, have been found to efficiently block agr and fsr QS systems, suggesting that the Epoxide group is involved in the mode of action of these QSIs. To address this notion, known natural Epoxide compounds, Cerulenin and Fosfomycin were examined for QSI activity for the agr and fsr systems in addition to in silico and SAR studies. As a result, most of investigated Epoxide containing antibiotics correlatively interfere with QSI activity for the agr and fsr systems under sublethal concentrations.

A practical guide to machine-learning scoring for structure-based virtual screening

Structure-based virtual screening (SBVS) via docking has been used to discover active molecules for a range of therapeutic targets. Chemical and protein data sets that contain integrated bioactivity information have increased both in number and in size. Artificial intelligence and, more concretely, its machine-learning (ML) branch, including deep learning, have effectively exploited these data sets to build scoring functions (SFs) for SBVS against targets with an atomic-resolution 3D model (e.g., generated by X-ray crystallography or predicted by AlphaFold2). Often outperforming their generic and non-ML counterparts, target-specific ML-based SFs represent the state of the art for SBVS. Here, we present a comprehensive and user-friendly protocol to build and rigorously evaluate these new SFs for SBVS. This protocol is organized into four sections: (i) using a public benchmark of a given target to evaluate an existing generic SF; (ii) preparing experimental data for a target from public repositories; (iii) partitioning data into a training set and a test set for subsequent target-specific ML modeling; and (iv) generating and evaluating target-specific ML SFs by using the prepared training-test partitions. All necessary code and input/output data related to three example targets (acetylcholinesterase, HMG-CoA reductase, and peroxisome proliferator-activated receptor-α) are available at https://github.com/vktrannguyen/MLSF-protocol , can be run by using a single computer within 1 week and make use of easily accessible software/programs (e.g., Smina, CNN-Score, RF-Score-VS and DeepCoy) and web resources. Our aim is to provide practical guidance on how to augment training data to enhance SBVS performance, how to identify the most suitable supervised learning algorithm for a data set, and how to build an SF with the highest likelihood of discovering target-active molecules within a given compound library.

Synthesis and Biological Evaluation of Some New 3-Aryl-2-thioxo-2,3-dihydroquinazolin-4(1H)-ones and 3-Aryl-2-(benzylthio)quinazolin-4(3H)-ones as Antioxidants; COX-2, LDHA, α-Glucosidase and α-Amylase Inhibitors; and Anti-Colon Carcinoma and Apoptosis-Inducing Agents

Oxidative stress, COX-2, LDHA and hyperglycemia are interlinked contributing pathways in the etiology, progression and metastasis of colon cancer. Additionally, dysregulated apoptosis in cells with genetic alternations leads to their progression in malignant transformation. Therefore, quinazolinones 3a-3h and 5a-5h were synthesized and evaluated as antioxidants, enzymes inhibitors and cytotoxic agents against LoVo and HCT-116 cells. Moreover, the most active cytotoxic derivatives were evaluated as apoptosis inducers. The results indicated that 3a, 3g and 5a were efficiently scavenged DPPH radicals with lowered IC50 values (mM) ranging from 0.165 ± 0.0057 to 0.191 ± 0.0099, as compared to 0.245 ± 0.0257 by BHT. Derivatives 3h, 5a and 5h were recognized as more potent dual inhibitors than quercetin against α-amylase and α-glucosidase, in addition to 3a, 3c, 3f and 5b-5f against α-amylase. Although none of the compounds demonstrated a higher efficiency than the reference inhibitors against COX-2 and LDHA, 3a and 3g were identified as the most active derivatives. Molecular docking studies were used to elucidate the binding affinities and binding interactions between the inhibitors and their target proteins. Compounds 3a and 3f showed cytotoxic activities, with IC50 values (µM) of 294.32 ± 8.41 and 383.5 ± 8.99 (LoVo), as well as 298.05 ± 13.26 and 323.59 ± 3.00 (HCT-116). The cytotoxicity mechanism of 3a and 3f could be attributed to the modulation of apoptosis regulators (Bax and Bcl-2), the activation of intrinsic and extrinsic apoptosis pathways via the upregulation of initiator caspases-8 and -9 as well as executioner caspase-3, and the arrest of LoVo and HCT-116 cell cycles in the G2/M and G1 phases, respectively. Lastly, the physicochemical, medicinal chemistry and ADMET properties of all compounds were predicted.

Lies and Liabilities: Computational Assessment of High-Throughput Screening Hits to Identify Artifact Compounds

Hits from high-throughput screening (HTS) of chemical libraries are often false positives due to their interference with assay detection technology. In response, we generated the largest publicly available library of chemical liabilities and developed "Liability Predictor," a free web tool to predict HTS artifacts. More specifically, we generated, curated, and integrated HTS data sets for thiol reactivity, redox activity, and luciferase (firefly and nano) activity and developed and validated quantitative structure-interference relationship (QSIR) models to predict these nuisance behaviors. The resulting models showed 58-78% external balanced accuracy for 256 external compounds per assay. QSIR models developed and validated herein identify nuisance compounds among experimental hits more reliably than do popular PAINS filters. Both the models and the curated data sets were implemented in "Liability Predictor," publicly available at https://liability.mml.unc.edu/. "Liability Predictor" may be used as part of chemical library design or for triaging HTS hits.

Descriptor-Driven de Novo Design Algorithms for DOCK6 Using RDKit

Structure-based methods that employ principles of de novo design can be used to construct small organic molecules from scratch using pre-existing fragment libraries to sample chemical space and are an important class of computational algorithms for drug-lead discovery. Here, we present a powerful new design method for DOCK6 that employs a Descriptor-Driven De Novo strategy (termed D3N) in which user-defined cheminformatics descriptors (and their target ranges) are calculated at each layer of growth using the open-source toolkit RDKit. The objective is to tailor ligand growth toward desirable regions of chemical space. The approach was extensively validated through: (1) comparison of cheminformatics descriptors computed using the new DOCK6/RDKit interface versus the standard Python/RDKit installation, (2) examination of descriptor distributions generated using D3N growth under different conditions (target ranges and environments), and (3) construction of ligands with very tight (pinpoint) descriptor ranges using clinically relevant compounds as a reference. Our testing confirms that the new DOCK6/RDKit integration is robust, showcases how the new D3N routines can be used to direct sampling around user-defined chemical spaces, and highlights the utility of on-the-fly descriptor calculations for ligand design to important drug targets.

Quinones as Neuroprotective Agents

Quinones can in principle be viewed as a double-edged sword in the treatment of neurodegenerative diseases, since they are often cytoprotective but can also be cytotoxic due to covalent and redox modification of biomolecules. Nevertheless, low doses of moderately electrophilic quinones are generally cytoprotective, mainly due to their ability to activate the Keap1/Nrf2 pathway and thus induce the expression of detoxifying enzymes. Some natural quinones have relevant roles in important physiological processes. One of them is coenzyme Q₁₀, which takes part in the oxidative phosphorylation processes involved in cell energy production, as a proton and electron carrier in the mitochondrial respiratory chain, and shows neuroprotective effects relevant to Alzheimer's and Parkinson's diseases. Additional neuroprotective quinones that can be regarded as coenzyme Q₁₀ analogues are idobenone, mitoquinone and plastoquinone. Other endogenous quinones with neuroprotective activities include tocopherol-derived quinones, most notably vatiquinone, and vitamin K. A final group of non-endogenous quinones with neuroprotective activity is discussed, comprising embelin, APX-3330, cannabinoid-derived quinones, asterriquinones and other indolylquinones, pyrroloquinolinequinone and its analogues, geldanamycin and its analogues, rifampicin quinone, memoquin and a number of hybrid structures combining quinones with amino acids, cholinesterase inhibitors and non-steroidal anti-inflammatory drugs.

Phenyl Pentyl Ketone and m-isobutyl Methoxy Benzoate Produced by Streptomyces Chrestomyceticus ADP4 are Potent Antimicrobial Agents Displaying Broad Spectrum Activities

Isolation and identification of two antimicrobial compounds, a phenyl pentyl ketone (CP1) and m-isobutyl methoxy benzoate (CP2), from Streptomyces chrestomyceticus ADP4 have been reported. Structure of the compounds were elucidated from analyses of spectral data that included LCMS/MS, NMR, FTIR and UV spectroscopies. Both the compounds displayed significant inhibition of albicans and non-albicans species of Candida (NAC) pathogens including C. auris, which is currently a pathogen of global concern. Also, the compounds showed potent antagonistic activity against Staphylococcus aureus, another significant human pathogen. No in-vitro cytotoxicity against HePG2 cells was observed with either of the compounds. Both displayed favourable drug likeness properties as determined by in-silico ADME and toxicological studies. Also, this is the first report on production of these anti-microbial compounds by an actinobacterium.

Supplementary Information

The online version contains supplementary material available at 10.1007/s12088-023-01068-7.

Molecular docking and dynamics simulation study of quinones and pyrones from Alternaria solani and Alternaria alternata with HSP90: an important therapeutic target of cancer

Although cancer continues to be one of the world's major causes of death, current cancer drugs have many serious side effects. There remains a need for new anticancer agents to overcome these shortcomings. Alternaria is one of the most widespread fungal genera, many species of which produce several classes of metabolites with potential polypharmacological activities. A few quinones and pyrones from Alternaria spp. have proven to exert cytotoxic effects against certain cancer cell lines, but their molecular mode of action is not known. The current study aimed to investigate the potential mechanisms that underlie the anticancer activity of a few selected quinones and pyrones from Alternaria solani and Alternaria alternata by molecular docking and dynamic simulation approaches. The selected metabolites were screened for their binding affinity to Heat shock protein 90 (HSP90), which is a known anticancer drug target. Molecular docking studies have revealed that Macrosporin, Altersolanol B, Fonsecin, and Neoaltenuene have good binding affinities with the target protein and the stabilities of the formed complexes were evaluated through molecular dynamics simulations. By analyzing the Root Mean Square Distance (RMSD), Root Mean Square Fluctuation (RMSF), and Principal Component Analysis (PCA) plots obtained from molecular dynamics simulations, this study shows that the complexes of all 4 lead molecules with target protein are stable over a 100 ns period. Molecular Mechanics Poisson-Boltzmann Surface Area (MM-PBSA) calculations were used to compute the binding free energies. The lead molecules were studied using in-silico analysis to determine their drug-likeness based on their Absorption, Distribution, Metabolism, Excretion and Toxicity (ADMET) and physicochemical properties. The results demonstrate that Macrosporin, Fonsecin, and Neoaltenuene could become promising anticancer molecules that target HSP90.Communicated by Ramaswamy H. Sarma.

A high-throughput screening campaign against PFKFB3 identified potential inhibitors with novel scaffolds

The growth of solid tumors depends on tumor vascularization and the endothelial cells (ECs) that line the lumen of blood vessels. ECs generate a large fraction of ATP through glycolysis, and elevation of their glycolytic activity is associated with angiogenic behavior in solid tumors. 6-Phosphofructo-2-kinase/fructose-2,6-bisphosphatase 3 (PFKFB3) positively regulates glycolysis via fructose-2/6-bisphosphate, the product of its kinase activity. Partial inhibition of glycolysis in tumor ECs by targeting PFKFB3 normalizes the otherwise abnormal tumor vessels, thereby reducing metastasis and improving the outcome of chemotherapy. Although a limited number of tool compounds exist, orally available PFKFB3 inhibitors are unavailable. In this study we conducted a high-throughput screening campaign against the kinase activity of PFKFB3, involving 250,240 chemical compounds. A total of 507 initial hits showing >50% inhibition at 20 µM were identified, 66 of them plus 1 analog from a similarity search consistently displayed low IC50 values (<10 µM). In vitro experiments yielded 22 nontoxic hits that suppressed the tube formation of primary human umbilical vein ECs at 10 µM. Of them, 15 exhibited binding affinity to PFKFB3 in surface plasmon resonance assays, including 3 (WNN0403-E003, WNN1352-H007 and WNN1542-F004) that passed the pan-assay interference compounds screening without warning flags. This study provides potential leads to the development of new PFKFB3 inhibitors.

An overview of cdc2-like kinase 1 (Clk1) inhibitors and their therapeutic indications

Over the past decade, Clk1 has been identified as a promising target for the treatment of various diseases, in which deregulated alternative splicing plays a role. First small molecules targeting Clk1 are in clinical trials for the treatment of solid cancer, where variants of oncogenic proteins derived from alternative splicing promote tumor progression. Since many infectious pathogens hi-jack the host cell's splicing machinery to ensure efficient replication, further indications in this area are under investigation, such as Influenza A, HIV-1 virus, and Trypanosoma infections, and more will likely be discovered in the future. In addition, Clk1 was found to contribute to the progression of Alzheimer's disease through causing an imbalance of tau splicing products. Interestingly, homozygous Clk1 knockout mice showed a rather mild phenotype, opposed to what might be expected in view of the profound role of Clk1 in alternative splicing. A major drawback of most Clk1 inhibitors is their insufficient selectivity; in particular, Dyrk kinases and haspin were frequently identified as off-targets, besides the other Clk isoforms. Only few inhibitors were shown to be selective over Dyrk1A and haspin, whereas no Clk1 inhibitor so far achieved selectivity over the Clk4 isoform. In this review, we carefully compiled all Clk1 inhibitors from the scientific literature and summarized their structure-activity relationships (SAR). In addition, we critically discuss the available selectivity data and describe the inhibitor's efficacy in cellular models, if reported. Thus, we provide a comprehensive overview on the current state of Clk1 drug discovery and highlight the most promising chemotypes.

Terminators or Guardians? Design, Synthesis, and Cytotoxicity Profiling of Chalcone-Sulfonamide Hybrids

With a "less is more" philosophy, a series of 15 chalcone-sulfonamide hybrids were designed anticipating synergistic anticancer activity. The aromatic sulfonamide moiety was included as a known direct inhibitor of carbonic anhydrase IX activity through its zinc chelating property. The chalcone moiety was incorporated as an electrophilic stressor to indirectly inhibit carbonic anhydrase IX cellular activity. Screening by the Developmental Therapeutics Program of the National Cancer Institute, NCI-60, revealed that 12 derivatives were potent inhibitors of cancer cell growth in multiple cell lines and were promoted to the five-dose screen. The cancer cell growth inhibition profile indicated sub- to two-digit micromolar potency (GI₅₀ down to 0.3 μM and LC₅₀ as low as 4 μM) against colorectal carcinoma cells, in particular. Unexpectedly, most compounds demonstrated low to moderate potency as direct inhibitors of carbonic anhydrase catalytic activity in vitro, with 4d being the most potent, having an average Ki value of 4 μM. Compound 4j showed ca. six-fold selectivity to carbonic anhydrase IX over the other tested isoforms in vitro. Cytotoxicity of both 4d and 4j in live HCT116, U251, and LOX IMVI cells under hypoxic conditions confirmed their targeting of carbonic anhydrase activity. Elevation of oxidative cellular stress was stipulated from the increase in Nrf2 and ROS levels in 4j-treated colorectal carcinoma, HCT116, cells compared to the control. Compound 4j arrested the cell cycle of HCT116 cells at the G1/S phase. In addition, both 4d and 4j showed up to 50-fold cancer cell selectivity compared to the non-cancerous HEK293T cells. Accordingly, this study presents 4d and 4j being new, synthetically accessible, simplistically designed derivatives as potential candidates to be further developed as anticancer therapeutics.

Antidepressant activity of phytochemicals of Mangifera indica seeds assisted by integrated computational analysis

Mangifera indica L., also known as mango, is a tropical fruit that belongs to the Anacardiaceae family and is prized for its juiciness, unique flavour, and worldwide popularity. The current study aimed to probe into antidepressant power (ADP) of MIS in animals and confirmation of ADP with in silico induced-fit molecular docking. The depression model was prepared by exposing mice to various stressors from 9:00 am to 2:00 pm during 42 days study period. MIS extract and fluoxetine were given daily for 30 min before exposing animals to stressors. ADP was evaluated by various behavioural tests and biochemical analysis. Results showed increased physical activity in mice under behavioural tests, plasma nitrite and malondialdehyde (MDA) levels and monoamine oxidase A (MAO-A) activity decreased dose-dependently in MIS treated mice and superoxide dismutases (SOD) levels increased in treated groups as compared to disease control. With the peculiar behaviour and significant interactions of the functional residues of target proteins with selected ligands along with the best absorption, distribution, metabolism, excretion, and toxicity (ADMET) properties, it is concluded that catechin could be the best MAO-A inhibitor at a binding energy of -8.85 kcal/mol, and two hydrogen bonds were generated with Cys406 (A) and Gly443 (A) residues of the active binding site of MAO-A enzyme. While catechin at -6.86 kcal/mol generated three hydrogen bonds with Ala263 (A) and Gly434 (A) residues of the active site of monoamine oxidase B (MAO-B) enzyme and stabilized the best conformation. Therefore, it is highly recommended to test the selected lead-like compound catechin in the laboratory with biological system analysis to confirm its activity as MAO-A and MAO-B inhibitors so it can be declared as one of the novel therapeutic options with anti-depressant activity. Our findings concluded that M. indica seeds could be a significant and alternative anti-depressant therapy.

Investigating N-arylpyrimidinamine (NAPA) compounds as early-stage inhibitors against human cytomegalovirus

Human cytomegalovirus (CMV) is a ubiquitous β-herpesvirus that establishes latent asymptomatic infections in healthy individuals but can cause serious infections in immunocompromised people, resulting in increased risk of morbidity and mortality. The current FDA-approved CMV drugs target late stages of the CMV life-cycle. While these drugs are effective in most cases, they have serious drawbacks, including poor oral bioavailability, dose-limiting toxicity, and a low barrier to resistance. Given the clinical relevance of CMV-associated diseases, novel therapies are needed. Thus, a novel class of compounds that inhibits the early stages of the CMV life-cycle was identified and found to block infection of different strains in physiologically relevant cell types. This class of compounds, N-arylpyrimidinamine (NAPA), demonstrated potent anti-CMV activity against ganciclovir-sensitive and -resistant strains in in vitro replication assays, a selectivity index >30, and favorable in vitro ADME properties. Mechanism of action studies demonstrated that NAPA compounds inhibit an early step of virus infection. NAPA compounds are specific inhibitors of cytomegaloviruses and exhibited limited anti-viral activity against other herpesviruses. Collectively, we have identified a novel class of CMV inhibitor that effectively limits viral infection and proliferation.

Flavonoids and saponins: What have we got or missed?

BACKGROUND

Flavonoids and saponins are important bioactive compounds that have attracted wide research interests. This review aims to summarise the state of the art of the pharmacology, toxicology and clinical efficacy of these compounds.

METHODS

Data were retrieved from PubMed, Cochrane Library, Web of Science, Proquest, CNKI, Chongqing VIP, Wanfang, NPASS and HIT 2.0 databases. Meta-analysis and systematic reviews were evaluated following the PRISMA guideline. Statistical analyses were conducted using SPSS23.0.

RESULTS

Rising research trends on flavonoids and saponins were observed since the 1990s and the 2000s, respectively. Studies on pharmacological targets and activities of flavonoids and saponins represent an important area of research advances over the past decade, and these important resources have been documented in open-access specialised databases and can be retrieved with ease. The rising research on flavonoids and saponins can be attributed, at least in part, to their links with some highly investigated fields of research, e.g., oxidative stress, inflammation and cancer; i.e., 6.88% and 3.03% of publications on oxidative stress cited by PubMed in 1990 - 2021 involved flavonoids and saponins, respectively, significantly higher than the percentage involving alkaloids (1.88%). The effects of flavonoids concern chronic venous insufficiency, cervical lesions, diabetes, rhinitis, dermatopathy, prostatitis, menopausal symptoms, angina pectoris, male pattern hair loss, lymphocytic leukaemia, gastrointestinal diseases and traumatic cerebral infarction, etc, while those of saponins may have impact on venous oedema in chronic deep vein incompetence, erectile dysfunction, acute impact injuries and systemic lupus erythematosus, etc. The volume of in vitro research appears way higher than in vivo and clinical studies, with only 10 meta-analyses and systematic reviews (involving 290 interventional and observational studies), and 36 clinical studies on flavonoids and saponins. Data are sorely needed on pharmacokinetics, in vitro pan-assay interferences, purity of tested compounds, interactions in complex herbal extracts, real impact of anti-oxidative strategies, and mid- and long-term toxicities. To fill these important gaps, further investigations are warranted. On the other hand, drug interactions may cause adverse effects but might also be useful for synergism, with the goals of enhancing effects or of detoxifying. Furthermore, the interactions between phytochemicals and the intestinal microbiota are worth investigating as the field may present a promising potential for novel drug development.

Computational Biology Dynamics of Mps1 Kinase Molecular Interactions with Isoflavones Reveals a Chemical Scaffold with Potential to Develop New Therapeutics for the Treatment of Cancer

The protein kinase Mps1 (monopolar spindle 1) is an important regulator of the Spindle Assembly Checkpoint (SAC), the evolutionary conserved checkpoint system of higher organisms that monitors the proper bipolar attachment of all chromosomes to the mitotic spindle during cell division. Defects in the catalytic activity and the transcription regulation of Mps1 are associated with genome instability, aneuploidy, and cancer. Moreover, multiple Mps1 missense and frameshift mutations have been reported in a wide range of types of cancer of different tissue origin. Due to these features, Mps1 arises as one promising drug target for cancer therapy. In this contribution, we developed a computational biology approach to study the dynamics of human Mps1 kinase interaction with isoflavones, a class of natural flavonoids, and compared their predicted mode of binding with that observed in the crystal structure of Mps1 in complex with reversine, a small-sized inhibitor of Mps1 and Aurora B kinases. We concluded that isoflavones define a chemical scaffold that can be used to develop new Mps1 inhibitors for the treatment of cancer associated with Mps1 amplification and aberrant chromosome segregation. In a broader context, the present report illustrates how modern chemoinformatics approaches can accelerate drug development in oncology.

Recent Advances of DprE1 Inhibitors against Mycobacterium tuberculosis: Computational Analysis of Physicochemical and ADMET Properties

Decaprenylphosphoryl-β-d-ribose 2'-epimerase (DprE1) is a critical flavoenzyme in Mycobacterium tuberculosis, catalyzing a vital step in the production of lipoarabinomannan and arabinogalactan, both of which are essential for cell wall biosynthesis. Due to its periplasmic localization, DprE1 is a susceptible target, and several compounds with diverse scaffolds have been discovered that inhibit this enzyme, covalently or noncovalently. We evaluated a total of ∼1519 DprE1 inhibitors disclosed in the literature from 2009 to April 2022 by performing an in-depth analysis of physicochemical descriptors and absorption, distribution, metabolism, excretion, and toxicity (ADMET), to gain new insights into these properties in DprE1 inhibitors. Several molecular properties that should facilitate the design and optimization of future DprE1 inhibitors are described, allowing for the development of improved analogues targeting M. tuberculosis.

Polyphenolic promiscuity, inflammation-coupled selectivity: Whether PAINs filters mask an antiviral asset

The Covid-19 pandemic has elicited much laboratory and clinical research attention on vaccines, mAbs, and certain small-molecule antivirals against SARS-CoV-2 infection. By contrast, there has been comparatively little attention on plant-derived compounds, especially those that are understood to be safely ingested at common doses and are frequently consumed in the diet in herbs, spices, fruits and vegetables. Examining plant secondary metabolites, we review recent elucidations into the pharmacological activity of flavonoids and other polyphenolic compounds and also survey their putative frequent-hitter behavior. Polyphenols, like many drugs, are glucuronidated post-ingestion. In an inflammatory milieu such as infection, a reversion back to the active aglycone by the release of β-glucuronidase from neutrophils and macrophages allows cellular entry of the aglycone. In the context of viral infection, virions and intracellular virus particles may be exposed to promiscuous binding by the polyphenol aglycones resulting in viral inhibition. As the mechanism's scope would apply to the diverse range of virus species that elicit inflammation in infected hosts, we highlight pre-clinical studies of polyphenol aglycones, such as luteolin, isoginkgetin, quercetin, quercetagetin, baicalein, curcumin, fisetin and hesperetin that reduce virion replication spanning multiple distinct virus genera. It is hoped that greater awareness of the potential spatial selectivity of polyphenolic activation to sites of pathogenic infection will spur renewed research and clinical attention for natural products antiviral assaying and trialing over a wide array of infectious viral diseases.

Development of novel cyanopyridines as PIM-1 kinase inhibitors with potent anti-prostate cancer activity: Synthesis, biological evaluation, nanoparticles formulation and molecular dynamics simulation

Recently, inhibition of PIM-1 enzyme is found as an effective route in the fight against proliferation of cancer. Herein, new cyano pyridines that target PIM-1 kinase were designed, synthesized, and biologically evaluated. Two prostate cell lines were used to examine each of the new compounds in vitro for anticancer activity, namely, PC-3 and DU-145. The cyanopyridine derivatives 2b, 3b, 4b, and 5b with an N,N-dimethyl phenyl group at the pyridine ring's 4-position showed considerable antitumor effect on the tested cell lines. Additionally, the high selectivity index revealed that these compounds were less cytotoxic to normal WI-38 cells. Furthermore, they exhibited strong inhibitory effect on PIM-1 having IC₅₀ = 0.248, 0.13, 0.326 and 0.245 μM, respectively. The most powerful derivatives2b, 3b, 4b, and 5b, were chosen for further examination of their inhibitory potential on both kinases (PIM-2 and PIM-3). Interestingly, upon loading compound 3b in a cubosomes formulation with nanometric size, improvements in cytotoxicity and inhibitory effect on PIM-1 kinase were observed. In silico ADME parameters study revealed that compound 3b is orally bioavailable without penetration to the blood-brain barrier. Further, the docking simulations revealed the ability of our potent compounds to well accommodate the PIM-1 kinase active site forming stable complexes. In a 150 ns MD simulation, the most powerful PIM-1 inhibitor 3b produced stable complex with the PIM-1 enzyme (RMSD = 1.76). Furthermore, the 3b-PIM-1 complex has the low binding free energy (-242.2 kJ/mol) according to the MM-PBSA calculations.

Secondary metabolites of Livistona decipiens as potential inhibitors of SARS-CoV-2

In late December 2019, a pandemic coronavirus disease 2019 (COVID-19) emerged in Wuhan, China and spread all over the globe. One of the promising therapeutic techniques of viral infection is to search for enzyme inhibitors among natural phytochemicals using molecular docking to obtain leads with the least side effects. The COVID-19 virus main protease (M^pro) is considered as an attractive target due to its pivotal role in controlling viral transcription and replication. Metabolic profiling of the crude extract of Livistona decipiens Becc. (Arecaceae) leaves and fruit dereplicated twelve metabolites using LC-HRESIMS. Molecular docking simulation and in silico ADME profiling of these annotated compounds proposed that tricin is a promising lead against COVID-19 virus M^pro. The alcoholic extract was shown to inhibit SARS-CoV-2 through in vitro culture and RT-PCR testing with EC₅₀ = 0.122 and 1.53 μg mL⁻¹ for leaves and fruit extracts, respectively, when compared with that of the FDA-approved anti-COVID-19 remdesivir (0.002 μg mL⁻¹). Preliminary steps were also performed including the 3CL-protease inhibition assay and cytotoxicity study. It is worthwhile to find a cheap, safe, natural source for promising anti-SARS-CoV-2 agents that can be further tested in vivo against the COVID-19 virus M^pro. This study provides scientific basis for demonstrating beneficial effects of L. decipiens application on human health during the corona pandemic.

Promising natural inhibitors for COVID-19.

A new ChEMBL dataset for the similarity-based target fishing engine FastTargetPred: Annotation of an exhaustive list of linear tetrapeptides

Drug discovery often requires the identification of off-targets as the binding of a compound to targets other than the intended target(s) can be beneficial in some cases or detrimental in other situations (e.g., binding to anti-targets). Such investigations are also of importance during the early stage of a project, for example when the target is not known (e.g., phenotypic screening). Target identification can be performed in-vitro, but various in-silico methods have also been developed in recent years to facilitate target identification and help generate ideas. FastTargetPred is one such approach, it is a freely available Python/C program that attempts to predict putative macromolecular targets (i.e., target fishing) for a single input small molecule query or an entire compound collection using established chemical similarity search approaches. Indeed, the putative macromolecular target(s) of a small chemical compound can be predicted by identifying ligands that are known experimentally to bind to some targets and that are structurally similar to the input query chemical compound. Therefore, this type of target fishing approach relies on a large collection of experimentally validated macromolecule-chemical compound binding data. The small chemical compounds can be described as molecular fingerprints encoding their structural characteristics as a vector. The published version of FastTargetPred used ligand-target binding data extracted from the release 25 (2019) of the ChEMBL database. Here we provide a new dataset for FastTargetPred extracted from the last ChEMBL release, namely, at the time of writing, ChEMBL29 (2021). Four fingerprints were computed (ECFP4, ECFP6, MACCS and PL) for the extracted compound dataset (714,780 unique ChEMBL29 compounds while the entire ChEMBL29 database contained about 2.1 million compounds). However, it was not possible to compute fingerprints for 19 molecules because of their unusual chemistry (complex macrocycles). These data files were then prepared so as to be compatible with FastTargetPred requirements. The 714,761 ChEMBL chemical compounds with computed fingerprints hit 6,477 macromolecular targets based on the selected criteria. For these ChEMBL compounds a ChEMBL target ID is reported and these target IDs were matched with the corresponding UniProt IDs. Thus, when available, the UniProt ID is provided, the protein UniProt name, the gene name, the organism as well as annotated involvement in diseases, gene ontology data, and cross-references to the Reactome pathway database. As short peptides can be of interest for drug discovery and chemical biology endeavours, we were interested in attempting to predict putative macromolecular targets for a previously reported exhaustive combination of peptides containing four natural amino acids (i.e., 20 × 20 × 20 × 20 = 160,000 linear tetrapeptides) using FastTargetPred and the presently generated ChEMBL29 dataset. With the parameters used, putative targets are reported for 63,944 unique query peptides. These target predictions are provided in two different searchable files with hyperlinks to the ChEMBL, UniProt and Reactome databases.

Biophysical Survey of Small-Molecule β-Catenin Inhibitors: A Cautionary Tale

The canonical Wingless-related integration site signaling pathway plays a critical role in human physiology, and its dysregulation can lead to an array of diseases. β-Catenin is a multifunctional protein within this pathway and an attractive yet challenging therapeutic target, most notably in oncology. This has stimulated the search for potent small-molecule inhibitors binding directly to the β-catenin surface to inhibit its protein–protein interactions and downstream signaling. Here, we provide an account of the claimed (and some putative) small-molecule ligands of β-catenin from the literature. Through in silico analysis, we show that most of these molecules contain promiscuous chemical substructures notorious for interfering with screening assays. Finally, and in line with this analysis, we demonstrate using orthogonal biophysical techniques that none of the examined small molecules bind at the surface of β-catenin. While shedding doubts on their reported mode of action, this study also reaffirms β-catenin as a prominent target in drug discovery.

Identification of Novel Potential Heparanase Inhibitors Using Virtual Screening

Heparanase (HPSE) is a mammalian endo-β-D-glucuronidase that cleaves heparan sulphate (HS) side chains of heparin sulphate proteoglycans (HSPG), a class of molecules composed of repeating polysulfated disaccharide units of glucosamine and hexuronic acid residues. HPSE controls the availability of growth factors, chemokines, lipoproteins and other bioactive molecules by degrading HS into smaller fractions, allowing the release of saccharide fragments that activate a plethora of signaling processes. HPSE overexpression has been correlated with tumor survival and metastasis as well as several diseases associated with chronic inflammation, including the ongoing COVID-19 pandemic caused by SARS-CoV-2. Thus, the search for molecules that could potentially inhibit HPSE has become increasingly relevant in the clinic. In this study, we have integrated a strategy that combines virtual screening and molecular docking of publicly available chemical databases to identify small compounds that can be developed into novel HPSE inhibitors. Structural rationalization of the interactions previously reported compounds led us to identify promising unexplored chemotypes. Here we show that these novel potential HPSE inhibitors present optimized in silico druggability and docking properties and may serve as pharmacological tools for the treatment of chronic and infectious diseases associated with chronic inflammation.

Targeting CB2 and TRPV1: Computational Approaches for the Identification of Dual Modulators

Both metabotropic (CBRs) and ionotropic cannabinoid receptors (ICRs) have implications in a range of neurological disorders. The metabotropic canonical CBRs CB1 and CB2 are highly implicated in these pathological events. However, selective targeting at CB2 versus CB1 offers optimized pharmacology due to the absence of psychoactive outcomes. The ICR transient receptor potential vanilloid type 1 (TRPV1) has also been reported to play a role in CNS disorders. Thus, activation of both targets, CB2 and TRPV1, offers a promising polypharmacological strategy for the treatment of neurological events including analgesia and neuroprotection. This brief research report aims to identify chemotypes with a potential dual CB2/TRPV1 profile. For this purpose, we have rationalized key structural features for activation and performed virtual screening at both targets using curated chemical libraries.

Fragment-Based Drug Discovery by NMR. Where Are the Successes and Where can It Be Improved?

Over the last century, the definitions of pharmaceutical drug and drug discovery have changed considerably. Evolving from an almost exclusively serendipitous approach, drug discovery nowadays involves several distinct, yet sometimes interconnected stages aimed at obtaining molecules able to interact with a defined biomolecular target, and triggering a suitable biological response. At each of the stages, a wide range of techniques are typically employed to obtain the results required to move the project into the next stage. High Throughput Screening (HTS) and Fragment Based Drug Design (FBDD) are the two main approaches used to identify drug-like candidates in the early stages of drug discovery. Nuclear Magnetic Resonance (NMR) spectroscopy has many applications in FBDD and is used extensively in industry as well as in academia. In this manuscript, we discuss the paths of both successful and unsuccessful molecules where NMR had a crucial part in their development. We specifically focus on the techniques used and describe strengths and weaknesses of each stage by examining several case studies. More precisely, we examine the development history from the primary screening to the final lead optimisation of AZD3839 interacting with BACE-1, ABT-199 interacting with BCL2/XL and S64315 interacting with MCL-1. Based on these studies, we derive observations and conclusions regarding the FBDD process by NMR and discuss its potential improvements.

Upregulation of BAX and caspase-3, as well as downregulation of Bcl-2 during treatment with indeno[1,2-b]quinoxalin derivatives, mediated apoptosis in human cancer cells

Cancer is the world's foremost cause of death. There are over 100 different forms of cancer. Cancers are frequently named after the organs or tissues in which they develop. As a part of our aim to develop promising anticancer agents, a series of new indeno[1,2-b]quinoxaline derivatives were synthesized. All of the synthesized compounds were tested for anticancer activity in vitro in three human cancer cell lines: the HCT-116 colon cancer cell line, the HepG-2 liver cancer cell line, and the MCF-7 breast cancer cell line. Among the tested derivatives, 2, 3, 5, 12, 21, and 22 showed exceptional antiproliferative activities against the three tested cell lines compared to the reference standard imatinib. These compounds were, therefore, selected for further investigations. Evaluation of their cytotoxicity against a normal human cell line (WI-38) was performed, to ensure their safety and selectivity (IC₅₀  > 92 μM). Then, induction of apoptosis by the most active compounds was found to be accomplished by downregulation of Bcl-2 and upregulation of BAX and caspase-3. After that, the most promising apoptotic compound that increases the caspase-3 and BAX expression and downregulates Bcl-2 activity (3) was assessed for its impact on the cell cycle distribution in HepG-2 cells: The most potent derivative (3) induced cell cycle arrest at the G2/M phase. Finally, in silico evaluation of the ADME properties indicated that compound 3 is orally bioavailable and can be readily synthesized on a large scale.

Promoting GAINs (Give Attention to Limitations in Assays) over PAINs Alerts: no PAINS, more GAINs

Many concepts and guidelines in medicinal chemistry have been introduced to aid in successful drug discovery and development. An example is the concept of Pan-Assay Interference Compounds (PAINS) and the elimination of such nuisance compounds from high-throughput screening (HTS) libraries. PAINs, along with other guidelines in medicinal chemistry, are like double-edged swords. If used appropriately, they may be beneficial for drug discovery and development. However, rigid and blind use of such concepts can hinder productivity. In this perspective, we introduce GAINS (give attention to limitations in assays) and highlight its relevance for successful drug discovery.

Gains from no real PAINS: Where 'Fair Trial Strategy' stands in the development of multi-target ligands

Compounds that selectively modulate multiple targets can provide clinical benefits and are an alternative to traditional highly selective agents for unique targets. High-throughput screening (HTS) for multitarget-directed ligands (MTDLs) using approved drugs, and fragment-based drug design has become a regular strategy to achieve an ideal multitarget combination. However, the unexpected presence of pan-assay interference compounds (PAINS) suspects in the development of MTDLs frequently results in nonspecific interactions or other undesirable effects leading to artefacts or false-positive data of biological assays. Publicly available filters can help to identify PAINS suspects; however, these filters cannot comprehensively conclude whether these suspects are "bad" or innocent. Additionally, these in silico approaches may inappropriately label a ligand as PAINS. More than 80% of the initial hits can be identified as PAINS by the filters if appropriate biochemical tests are not used resulting in false positive data that are unacceptable for medicinal chemists in manuscript peer review and future studies. Therefore, extensive offline experiments should be used after online filtering to discriminate "bad" PAINS and avoid incorrect evaluation of good scaffolds. We suggest that the use of "Fair Trial Strategy" to identify interesting molecules in PAINS suspects to provide certain structure‒function insight in MTDL development.

A practical guide to large-scale docking

Structure-based docking screens of large compound libraries have become common in early drug and probe discovery. As computer efficiency has improved and compound libraries have grown, the ability to screen hundreds of millions, and even billions, of compounds has become feasible for modest-sized computer clusters. This allows the rapid and cost-effective exploration and categorization of vast chemical space into a subset enriched with potential hits for a given target. To accomplish this goal at speed, approximations are used that result in undersampling of possible configurations and inaccurate predictions of absolute binding energies. Accordingly, it is important to establish controls, as are common in other fields, to enhance the likelihood of success in spite of these challenges. Here we outline best practices and control docking calculations that help evaluate docking parameters for a given target prior to undertaking a large-scale prospective screen, with exemplification in one particular target, the melatonin receptor, where following this procedure led to direct docking hits with activities in the subnanomolar range. Additional controls are suggested to ensure specific activity for experimentally validated hit compounds. These guidelines should be useful regardless of the docking software used. Docking software described in the outlined protocol (DOCK3.7) is made freely available for academic research to explore new hits for a range of targets.

Polyhydroxybenzoic acid derivatives as potential new antimalarial agents

With more than 200 million cases and 400,000 related deaths, malaria remains one of the deadliest infectious diseases of 2021. Unfortunately, despite the availability of efficient treatments, we have observed an increase in people infected with malaria since 2015 (from 211 million in 2015 to 229 million in 2019). This trend could partially be due to the development of resistance to all the current drugs. Therefore, there is an urgent need for new alternatives. We have, thus, selected common natural scaffolds, polyhydroxybenzoic acids, and synthesized a library of derivatives to better understand the structure-activity relationships explaining their antiplasmodial effect. Only gallic acid derivatives showed a noticeable potential for further developments. Indeed, they showed a selective inhibitory effect on Plasmodium (IC₅₀ ~20 µM, SI > 5) often associated with interesting water solubility. Moreover, this has confirmed the critical importance of free phenolic functions (pyrogallol moiety) for the antimalarial effect. Methyl 4-benzoxy-3,5-dihydroxybenzoate (39) has, for the first time, been recognized as a potential lead for future research because of its marked inhibitory activity against Plasmodium falciparum and its significant hydrosolubility (3.72 mM).

Artificial intelligence to deep learning: machine intelligence approach for drug discovery

Drug designing and development is an important area of research for pharmaceutical companies and chemical scientists. However, low efficacy, off-target delivery, time consumption, and high cost impose a hurdle and challenges that impact drug design and discovery. Further, complex and big data from genomics, proteomics, microarray data, and clinical trials also impose an obstacle in the drug discovery pipeline. Artificial intelligence and machine learning technology play a crucial role in drug discovery and development. In other words, artificial neural networks and deep learning algorithms have modernized the area. Machine learning and deep learning algorithms have been implemented in several drug discovery processes such as peptide synthesis, structure-based virtual screening, ligand-based virtual screening, toxicity prediction, drug monitoring and release, pharmacophore modeling, quantitative structure-activity relationship, drug repositioning, polypharmacology, and physiochemical activity. Evidence from the past strengthens the implementation of artificial intelligence and deep learning in this field. Moreover, novel data mining, curation, and management techniques provided critical support to recently developed modeling algorithms. In summary, artificial intelligence and deep learning advancements provide an excellent opportunity for rational drug design and discovery process, which will eventually impact mankind. The primary concern associated with drug design and development is time consumption and production cost. Further, inefficiency, inaccurate target delivery, and inappropriate dosage are other hurdles that inhibit the process of drug delivery and development. With advancements in technology, computer-aided drug design integrating artificial intelligence algorithms can eliminate the challenges and hurdles of traditional drug design and development. Artificial intelligence is referred to as superset comprising machine learning, whereas machine learning comprises supervised learning, unsupervised learning, and reinforcement learning. Further, deep learning, a subset of machine learning, has been extensively implemented in drug design and development. The artificial neural network, deep neural network, support vector machines, classification and regression, generative adversarial networks, symbolic learning, and meta-learning are examples of the algorithms applied to the drug design and discovery process. Artificial intelligence has been applied to different areas of drug design and development process, such as from peptide synthesis to molecule design, virtual screening to molecular docking, quantitative structure-activity relationship to drug repositioning, protein misfolding to protein-protein interactions, and molecular pathway identification to polypharmacology. Artificial intelligence principles have been applied to the classification of active and inactive, monitoring drug release, pre-clinical and clinical development, primary and secondary drug screening, biomarker development, pharmaceutical manufacturing, bioactivity identification and physiochemical properties, prediction of toxicity, and identification of mode of action.

Tumor pyruvate kinase M2 modulators: a comprehensive account of activators and inhibitors as anticancer agents

Pyruvate kinase M2 (PKM2) catalyzes the conversion of phosphoenolpyruvate (PEP) to pyruvate. It plays a central role in the metabolic reprogramming of cancer cells and is expressed in most human tumors. It is essential in indiscriminate proliferation, survival, and tackling apoptosis in cancer cells. This positions PKM2 as a hot target in cancer therapy. Despite its well-known structure and several reported modulators targeting PKM2 as activators or inhibitors, a comprehensive review focusing on such modulators is lacking. Herein we summarize modulators of PKM2, the assays used to detect their potential, the preferable tense (T) and relaxed (R) states in which the enzyme resides, lacunae in existing modulators, and several strategies that may lead to effective anticancer drug development targeting PKM2.

Small-Molecule Inhibitors of the Coronavirus Spike: ACE2 Protein-Protein Interaction as Blockers of Viral Attachment and Entry for SARS-CoV-2

Inhibitors of the protein-protein interaction (PPI) between the SARS-CoV-2 spike protein and human ACE2 (hACE2), which acts as a ligand-receptor pair that initiates the viral attachment and cellular entry of this coronavirus causing the ongoing COVID-19 pandemic, are of considerable interest as potential antiviral agents. While blockade of such PPIs with small molecules is more challenging than that with antibodies, small-molecule inhibitors (SMIs) might offer alternatives that are less strain- and mutation-sensitive, suitable for oral or inhaled administration, and more controllable/less immunogenic. Here, we report the identification of SMIs of this PPI by screening our compound library focused around the chemical space of organic dyes. Among promising candidates identified, several dyes (Congo red, direct violet 1, Evans blue) and novel druglike compounds (DRI-C23041, DRI-C91005) inhibited the interaction of hACE2 with the spike proteins of SARS-CoV-2 as well as SARS-CoV with low micromolar activity in our cell-free ELISA-type assays (IC50's of 0.2-3.0 μM), whereas control compounds, such as sunset yellow FCF, chloroquine, and suramin, showed no activity. Protein thermal shift assays indicated that the SMIs of interest identified here bind SARS-CoV-2-S and not hACE2. While dyes seemed to be promiscuous inhibitors, DRI-C23041 showed some selectivity and inhibited the entry of two different SARS-CoV-2-S expressing pseudoviruses into hACE2-expressing cells in a concentration-dependent manner with low micromolar IC50's (6-7 μM). This provides proof-of-principle evidence for the feasibility of small-molecule inhibition of PPIs critical for SARS-CoV-2 attachment/entry and serves as a first guide in the search for SMI-based alternative antiviral therapies for the prevention and treatment of diseases caused by coronaviruses in general and COVID-19 in particular.