A common mechanism underlying promiscuous inhibitors from virtual and high-throughput screening

Colloidal Aggregation Confounds Cell-Based Covid-19 Antiviral Screens

Colloidal aggregation is one of the largest contributors to false positives in early drug discovery. Here, we consider aggregation's role in cell-based infectivity assays in Covid-19 drug repurposing. We investigated the potential aggregation of 41 drug candidates reported as SARs-CoV-2 entry inhibitors. Of these, 17 formed colloidal particles by dynamic light scattering and exhibited detergent-dependent enzyme inhibition. To evaluate the impact of aggregation on antiviral efficacy in cells, we presaturated the colloidal drug suspensions with BSA or spun them down by centrifugation and measured the effects on spike pseudovirus infectivity. Antiviral potencies diminished by at least 10-fold following both BSA and centrifugation treatments, supporting a colloid-based mechanism. Aggregates induced puncta of the labeled spike protein in fluorescence microscopy, consistent with sequestration of the protein on the colloidal particles. These observations suggest that colloidal aggregation is common among cell-based antiviral drug repurposing and offers rapid counter-screens to detect and eliminate these artifacts.

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/.

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.

AI is a viable alternative to high throughput screening: a 318-target study

High throughput screening (HTS) is routinely used to identify bioactive small molecules. This requires physical compounds, which limits coverage of accessible chemical space. Computational approaches combined with vast on-demand chemical libraries can access far greater chemical space, provided that the predictive accuracy is sufficient to identify useful molecules. Through the largest and most diverse virtual HTS campaign reported to date, comprising 318 individual projects, we demonstrate that our AtomNet® convolutional neural network successfully finds novel hits across every major therapeutic area and protein class. We address historical limitations of computational screening by demonstrating success for target proteins without known binders, high-quality X-ray crystal structures, or manual cherry-picking of compounds. We show that the molecules selected by the AtomNet® model are novel drug-like scaffolds rather than minor modifications to known bioactive compounds. Our empirical results suggest that computational methods can substantially replace HTS as the first step of small-molecule drug discovery.

Molecular dynamics simulations as a guide for modulating small molecule aggregation

Small colloidally aggregating molecules (SCAMs) can be problematic for biological assays in drug discovery campaigns. However, the self-associating properties of SCAMs have potential applications in drug delivery and analytical biochemistry. Consequently, the ability to predict the aggregation propensity of a small organic molecule is of considerable interest. Chemoinformatics-based filters such as ChemAGG and Aggregator Advisor offer rapid assessment but are limited by the assay quality and structural diversity of their training set data. Complementary to these tools, we explore here the ability of molecular dynamics (MD) simulations as a physics-based method capable of predicting the aggregation propensity of diverse chemical structures. For a set of 32 molecules, using simulations of 100 ns in explicit solvent, we find a success rate of 97% (one molecule misclassified) as opposed to 75% by Aggregator Advisor and 72% by ChemAGG. These short timescale MD simulations are representative of longer microsecond trajectories and yield an informative spectrum of aggregation propensities across the set of solutes, capturing the dynamic behaviour of weakly aggregating compounds. Implicit solvent simulations using the generalized Born model were less successful in predicting aggregation propensity. MD simulations were also performed to explore structure-aggregation relationships for selected molecules, identifying chemical modifications that reversed the predicted behaviour of a given aggregator/non-aggregator compound. While lower throughput than rapid cheminformatics-based SCAM filters, MD-based prediction of aggregation has potential to be deployed on the scale of focused subsets of moderate size, and, depending on the target application, provide guidance on removing or optimizing a compound's aggregation propensity.

On the origins of SARS-CoV-2 main protease inhibitors

In order to address the world-wide health challenge caused by the COVID-19 pandemic, the 3CL protease/SARS-CoV-2 main protease (SARS-CoV-2-Mpro) coded by its nsp5 gene became one of the biochemical targets for the design of antiviral drugs. In less than 3 years of research, 4 inhibitors of SARS-CoV-2-Mpro have actually been authorized for COVID-19 treatment (nirmatrelvir, ensitrelvir, leritrelvir and simnotrelvir) and more such as EDP-235, FB-2001 and STI-1558/Olgotrelvir or five undisclosed compounds (CDI-988, ASC11, ALG-097558, QLS1128 and H-10517) are undergoing clinical trials. This review is an attempt to picture this quite unprecedented medicinal chemistry feat and provide insights on how these cysteine protease inhibitors were discovered. Since many series of covalent SARS-CoV-2-Mpro inhibitors owe some of their origins to previous work on other proteases, we first provided a description of various inhibitors of cysteine-bearing human caspase-1 or cathepsin K, as well as inhibitors of serine proteases such as human dipeptidyl peptidase-4 or the hepatitis C protein complex NS3/4A. This is then followed by a description of the results of the approaches adopted (repurposing, structure-based and high throughput screening) to discover coronavirus main protease inhibitors.

Design, Synthesis, and Biological Evaluation of Water-Soluble Prodrugs of C5-Curcuminoid GO-Y030 Based on Reversible Thia-Michael Reaction

Although curcumin and its analogs exhibit anticancer activity, they are still not used as anticancer drugs because of their water insolubility and extremely poor bioavailability. This study describes the development of water-soluble prodrugs of GO-Y030, a potent antitumor C5-curcuminoid, in an attempt to enhance its bioavailability. These prodrugs release the parent compound via a retro-thia-Michael reaction. To endow sufficient hydrophilicity onto GO-Y030 via a single thia-Michael reaction of an aqueous entity, we used a modified glycoconjugate with a thiol group. The water-solubilizing motif was installed on GO-Y030 by the thia-Michael reaction of propargyl-polyethylene glycol (PEG)-thiol and subsequent click chemistry (CuAAC) reaction with 1-glycosyl azide. Turbidity measurements revealed a significantly improved water solubility of the prodrugs, demonstrating that disaccharide conjugates were completely dissolved in water at 100 µM. Their cytotoxicity was comparable to that of the parent compound GO-Y030, indicating the gradual in situ release of GO-Y030. The release of GO-Y030 from GO-Y199 via the retro-thia-Michael reaction was demonstrated through a degradation study in water. Our retro-thia-Michael reaction-based prodrug system can be used for targeting cancer cells.

Discovery and structural optimization of a new series of N-acyl-2-aminobenzothiazole as inhibitors of Zika virus

Zika virus infection is associated to severe diseases such as congenital microcephaly and Zika fever causing serious harm to humans and special concern to health systems in low-income countries. Currently, there are no approved drugs against the virus, and the development of anti-Zika virus drugs is thus urgent. The present investigation describes the discovery and hit expansion of a N-acyl-2-aminobenzothiazole series of compounds against Zika virus replication. A structure-activity relationship study was obtained with the synthesis and evaluation of anti-Zika virus activity and cytotoxicity on Vero cells of nineteen derivatives. The three optimized compounds were 2.2-fold more potent than the initial hit and 20.9, 7.7 and 6.4-fold more selective. Subsequent phenotypic and biochemical assays were performed to evidence whether non-structural proteins, such as the complex NS2B-NS3pro, are related to the mechanism of action of the most active compounds.

Confronting the Threat: Designing Highly Effective bis-Benzimidazolium Agents to Overcome Biofilm Persistence and Antimicrobial Resistance

The objective of this study is to take the initial steps toward developing novel antibiotics to counteract the escalating problem of antimicrobial and bacterial persistence, particularly in relation to biofilms. Our approach involves emulating the structural characteristics of cationic antimicrobial peptides. To circumvent resistance development, we have designed a library of bis-benzimidazolium salts that selectively target the microbial membranes in a nonspecific manner. To explore their structure-activity relationship, we conducted experiments using these compounds on various pathogens known for their resistance to conventional antibiotics, including Gram-positive methicillin-resistant Staphylococcus aureus (MRSA), vancomycin-resistant Enterococcus faecium (VRE), and Gram-negative Escherichia coli (E. coli). Notably, two bis-benzimidazolium salts exhibited robust antimicrobial activity while maintaining a high level of selectivity compared with mammalian cells. Our investigations revealed significant antibiofilm activity, as these compounds rapidly acted against established biofilms. In addition, bis-benzimidazolium compounds exhibited consistent results in resistance development and cross-resistance studies. Consequently, amphiphilic bis-benzimidazolium salts hold promise as potential candidates to combat resistance-associated infections.

Colloidal aggregation confounds cell-based Covid-19 antiviral screens

Colloidal aggregation is one of the largest contributors to false-positives in early drug discovery and chemical biology. Much work has focused on its impact on pure-protein screens; here we consider aggregations role in cell-based infectivity assays in Covid-19 drug repurposing. We began by investigating the potential aggregation of 41 drug candidates reported as SARs-CoV-2 entry inhibitors. Of these, 17 formed colloidal-particles by dynamic light scattering and exhibited detergent-dependent enzyme inhibition. To evaluate antiviral efficacy of the drugs in cells we used spike pseudotyped lentivirus and pre-saturation of the colloids with BSA. The antiviral potency of the aggregators was diminished by at least 10-fold and often entirely eliminated in the presence of BSA, suggesting antiviral activity can be attributed to the non-specific nature of the colloids. In confocal microscopy, the aggregates induced fluorescent puncta of labeled spike protein, consistent with sequestration of the protein on the colloidal particles. Addition of either non-ionic detergent or of BSA disrupted these puncta. These observations suggest that colloidal aggregation is common among cell-based anti-viral drug repurposing, and perhaps cell-based assays more broadly, and offers rapid counter-screens to detect and eliminate these artifacts, allowing the community invest resources in compounds with true potential as a Covid-19 therapeutic.

Unraveling the unexpected aggregation behavior of Pyrazole-Based compounds Targeting Mycobacterium tuberculosis UDP-Galactopyranose mutase

A pyrazole-based compound, MS208, was previously identified as an inhibitor of UDP-Galactopyranose Mutase from Mycobacterium tuberculosis (MtUGM). Targeting this enzyme is a novel therapeutic strategy for the development of new antituberculosis agents because MtUGM is an essential enzyme for the bacterial cell wall synthesis and it is not present in human. It was proposed that MS208 targets an allosteric site in MtUGM as MS208 followed a mixed inhibition model. DA10, an MS208 analogue, showed competitive inhibition rather than mixed inhibition. In this paper, we have used an integrated biophysical approach, including thermal shift assays, dynamic light scattering and nuclear magnetic resonance experiments, to show that MS208 and many analogues displayed unexpected aggregation behavior against MtUGM.

The Ethics of Publishing Biomedical and Natural Products Research

Given that the essence of Science is a search for the truth, one might expect that those identifying as scientists would be conscientious and observant of the demands this places on them. However, that expectation is not fulfilled universally as, not too surprisingly, egregious examples of unethical behavior appear and are driven by money, personal ambition, performance pressure, and other incentives. The reproducibility-, fact-, and truth-oriented modus operandi of Science has come to face a variety of challenges. Organized into 11 cases, this article outlines examples of compromised integrity from borderline to blatant unethical behavior that disgrace our profession unnecessarily. Considering technological developments in neural networks/artificial intelligence, a host of factors are identified as impacting Good Ethical Practices. The goal is manifold: to raise awareness and offer perspectives for refocusing on Science and true scientific evidence; to trigger discussion and developments that strengthen ethical behavior; to foster the recognition of the beauty, simplicity, and rewarding nature of scientific integrity; and to highlight the originality of intelligence.

PEG: The Magic Bullet for Biophysical Analysis of Highly Aggregating Small Molecules in Aqueous Solutions

Biophysical research plays a crucial role in drug discovery, but many druglike molecules are poorly soluble and prone to aggregation, making their analysis challenging and susceptible to artifacts. To address this issue, we propose an approach that uses poly(ethylene glycol) (PEG) as an excipient in aqueous buffers to reduce the propensity of small molecules to aggregate. We show how PEG allows us to measure the thermodynamics of a complex formed by a heterobifunctional Small Molecule (hSM) that brings two proteins together. Our model accounts for all of the equilibrium states of the small molecule in solution, resulting in more precise parameters for describing how the proteins and the ligand interact. These precise parameters are important for designing better lead molecules.

Drug Self-Aggregation into Nano-Entities Has the Potential to Induce Immune Responses

The free-state solution behaviors of small molecules profoundly affect their respective properties. It is becoming more obvious that compounds can adopt a three-phase equilibrium when placed in an aqueous solution, among soluble-lone molecule form, self-assembled aggregate form (nano-entities), and solid precipitate form. Recently, correlations have emerged between the existence of self-assemblies into drug nano-entities and unintended side effects. This report describes our pilot study involving a selection of drugs and dyes to explore if there may be a correlation between the existence of drug nano-entities and immune responses. We first implement practical strategies for detecting the drug self-assemblies using a combination of nuclear magnetic resonance (NMR), dynamic light scattering (DLS), transmission electron microscopy (TEM), and confocal microscopy. We then used enzyme-linked immunosorbent assays (ELISA) to monitor the modulation of immune responses on two cellular models, murine macrophage and human neutrophils, upon exposure to the drugs and dyes. The results suggest that exposure to some aggregates correlated with an increase in IL-8 and TNF-α in these model systems. Given this pilot study, further correlations merit pursuing on a larger scale given the importance and potential impact of drug-induced immune-related side effects.

Promiscuity in drug discovery on the verge of the structural revolution: recent advances and future chances

INTRODUCTION

Promiscuity denotes the ability of ligands and targets to specifically interact with multiple binding partners. Despite negative aspects like side effects, promiscuity is receiving increasing attention in drug discovery as it can enhance drug efficacy and provides a molecular basis for drug repositioning. The three-dimensional structure of ligand-target complexes delivers exclusive insights into the molecular mechanisms of promiscuity and structure-based methods enable the identification of promiscuous interactions. With the recent breakthrough in protein structure prediction, novel possibilities open up to reveal unknown connections in ligand-target interaction networks.

AREAS COVERED

This review highlights the significance of structure in the identification and characterization of promiscuity and evaluates the potential of protein structure prediction to advance our knowledge of drug-target interaction networks. It discusses the definition and relevance of promiscuity in drug discovery and explores different approaches to detecting promiscuous ligands and targets.

EXPERT OPINION

Examination of structural data is essential for understanding and quantifying promiscuity. The recent advancements in structure prediction have resulted in an abundance of targets that are well-suited for structure-based methods like docking. In silico approaches may eventually completely transform our understanding of drug-target networks by complementing the millions of predicted protein structures with billions of predicted drug-target interactions.

Structure-Based Discovery of Inhibitors of the SARS-CoV-2 Nsp14 N7-Methyltransferase

An under-explored target for SARS-CoV-2 is the S-adenosyl methionine (SAM)-dependent methyltransferase Nsp14, which methylates the N7-guanosine of viral RNA at the 5'-end, allowing the virus to evade host immune response. We sought new Nsp14 inhibitors with three large library docking strategies. First, up to 1.1 billion lead-like molecules were docked against the enzyme's SAM site, leading to three inhibitors with IC50 values from 6 to 50 μM. Second, docking a library of 16 million fragments revealed 9 new inhibitors with IC50 values from 12 to 341 μM. Third, docking a library of 25 million electrophiles to covalently modify Cys387 revealed 7 inhibitors with IC50 values from 3.5 to 39 μM. Overall, 32 inhibitors encompassing 11 chemotypes had IC50 values < 50 μM and 5 inhibitors in 4 chemotypes had IC50 values < 10 μM. These molecules are among the first non-SAM-like inhibitors of Nsp14, providing starting points for future optimization.

Identification of multidentate tyrosyl-DNA phosphodiesterase 1 (TDP1) inhibitors that simultaneously access the DNA, protein and catalytic-binding sites by oxime diversification

Tyrosyl-DNA phosphodiesterase 1 (TDP1) is a member of the phospholipase D family that can downregulate the anticancer effects of the type I topoisomerase (TOP1) inhibitors by hydrolyzing the 3'-phosphodiester bond between DNA and the TOP1 residue Y723 in the critical stalled intermediate that is the foundation of TOP1 inhibitor mechanism of action. Thus, TDP1 antagonists are attractive as potential enhancers of TOP1 inhibitors. However, the open and extended nature of the TOP1-DNA substrate-binding region has made the development of TDP1 inhibitors extremely challenging. In this study, starting from our recently identified small molecule microarray (SMM)-derived TDP1-inhibitory imidazopyridine motif, we employed a click-based oxime protocol to extend the parent platform into the DNA and TOP1 peptide substrate-binding channels. We applied one-pot Groebke-Blackburn-Bienayme multicomponent reactions (GBBRs) to prepare the needed aminooxy-containing substrates. By reacting these precursors with approximately 250 aldehydes in microtiter format, we screened a library of nearly 500 oximes for their TDP1 inhibitory potencies using an in vitro florescence-based catalytic assay. Select hits were structurally explored as their triazole- and ether-based isosteres. We obtained crystal structures of two of the resulting inhibitors bound to the TDP1 catalytic domain. The structures reveal that the inhibitors form hydrogen bonds with the catalytic His-Lys-Asn triads ("HKN" motifs: H263, K265, N283 and H493, K495, N516), while simultaneously extending into both the substrate DNA and TOP1 peptide-binding grooves. This work provides a structural model for developing multivalent TDP1 inhibitors capable of binding in a tridentate fashion with a central component situated within the catalytic pocket and extensions that project into both the DNA and TOP1 peptide substrate-binding regions.

High-Throughput Screening of Natural Product and Synthetic Molecule Libraries for Antibacterial Drug Discovery

Due to the continued emergence of resistance and a lack of new and promising antibiotics, bacterial infection has become a major public threat. High-throughput screening (HTS) allows rapid screening of a large collection of molecules for bioactivity testing and holds promise in antibacterial drug discovery. More than 50% of the antibiotics that are currently available on the market are derived from natural products. However, with the easily discoverable antibiotics being found, finding new antibiotics from natural sources has seen limited success. Finding new natural sources for antibacterial activity testing has also proven to be challenging. In addition to exploring new sources of natural products and synthetic biology, omics technology helped to study the biosynthetic machinery of existing natural sources enabling the construction of unnatural synthesizers of bioactive molecules and the identification of molecular targets of antibacterial agents. On the other hand, newer and smarter strategies have been continuously pursued to screen synthetic molecule libraries for new antibiotics and new druggable targets. Biomimetic conditions are explored to mimic the real infection model to better study the ligand-target interaction to enable the designing of more effective antibacterial drugs. This narrative review describes various traditional and contemporaneous approaches of high-throughput screening of natural products and synthetic molecule libraries for antibacterial drug discovery. It further discusses critical factors for HTS assay design, makes a general recommendation, and discusses possible alternatives to traditional HTS of natural products and synthetic molecule libraries for antibacterial drug discovery.

Synthetic Ionizable Colloidal Drug Aggregates Enable Endosomal Disruption

Colloidal drug aggregates enable the design of drug-rich nanoparticles; however, the efficacy of stabilized colloidal drug aggregates is limited by entrapment in the endo-lysosomal pathway. Although ionizable drugs are used to elicit lysosomal escape, this approach is hindered by toxicity associated with phospholipidosis. It is hypothesized that tuning the pK_a of the drug would enable endosomal disruption while avoiding phospholipidosis and minimizing toxicity. To test this idea, 12 analogs of the nonionizable colloidal drug fulvestrant are synthesized with ionizable groups to enable pH-dependent endosomal disruption while maintaining bioactivity. Lipid-stabilized fulvestrant analog colloids are endocytosed by cancer cells, and the pK_a of these ionizable colloids influenced the mechanism of endosomal and lysosomal disruption. Four fulvestrant analogs-those with pK_a values between 5.1 and 5.7-disrupted endo-lysosomes without measurable phospholipidosis. Thus, by manipulating the pK_a of colloid-forming drugs, a tunable and generalizable strategy for endosomal disruption is established.

Ligand fishing as a tool to screen natural products with anticancer potential

Cancer is the second leading cause of death in the world and its incidence is expected to increase with the aging of the world's population and globalization of risk factors. Natural products and their derivatives have provided a significant number of approved anticancer drugs and the development of robust and selective screening assays for the identification of lead anticancer natural products are essential in the challenge of developing personalized targeted therapies tailored to the genetic and molecular characteristics of tumors. To this end, a ligand fishing assay is a remarkable tool to rapidly and rigorously screen complex matrices, such as plant extracts, for the isolation and identification of specific ligands that bind to relevant pharmacological targets. In this paper, we review the application of ligand fishing with cancer-related targets to screen natural product extracts for the isolation and identification of selective ligands. We provide critical analysis of the system configurations, targets, and key phytochemical classes related to the field of anticancer research. Based on the data collected, ligand fishing emerges as a robust and powerful screening system for the rapid discovery of new anticancer drugs from natural resources. It is currently an underexplored strategy according to its considerable potential.

Screening the Pathogen Box to Discover and Characterize New Cruzain and TbrCatL Inhibitors

Chagas disease and Human African Trypanosomiasis, caused by Trypanosoma cruzi and T. brucei, respectively, pose relevant health challenges throughout the world, placing 65 to 70 million people at risk each. Given the limited efficacy and severe side effects associated with current chemotherapy, new drugs are urgently needed for both diseases. Here, we report the screening of the Pathogen Box collection against cruzain and TbrCatL, validated targets for Chagas disease and Human African Trypanosomiasis, respectively. Enzymatic assays were applied to screen 400 compounds, validate hits, determine IC₅₀ values and, when possible, mechanisms of inhibition. In this case, 12 initial hits were obtained and ten were prioritized for follow-up. IC₅₀ values were obtained for six of them (hit rate = 1.5%) and ranged from 0.46 ± 0.03 to 27 ± 3 µM. MMV687246 was found to be a mixed inhibitor of cruzain (K_i = 57 ± 6 µM) while MMV688179 was found to be a competitive inhibitor of cruzain with a nanomolar potency (K_i = 165 ± 63 nM). A putative binding mode for MMV688179 was obtained by docking. The six hits discovered against cruzain and TbrCatL are of great interest for further optimization by the medicinal chemistry community.

Computational Approaches to Enzyme Inhibition by Marine Natural Products in the Search for New Drugs

The exploration of biologically relevant chemical space for the discovery of small bioactive molecules present in marine organisms has led not only to important advances in certain therapeutic areas, but also to a better understanding of many life processes. The still largely untapped reservoir of countless metabolites that play biological roles in marine invertebrates and microorganisms opens new avenues and poses new challenges for research. Computational technologies provide the means to (i) organize chemical and biological information in easily searchable and hyperlinked databases and knowledgebases; (ii) carry out cheminformatic analyses on natural products; (iii) mine microbial genomes for known and cryptic biosynthetic pathways; (iv) explore global networks that connect active compounds to their targets (often including enzymes); (v) solve structures of ligands, targets, and their respective complexes using X-ray crystallography and NMR techniques, thus enabling virtual screening and structure-based drug design; and (vi) build molecular models to simulate ligand binding and understand mechanisms of action in atomic detail. Marine natural products are viewed today not only as potential drugs, but also as an invaluable source of chemical inspiration for the development of novel chemotypes to be used in chemical biology and medicinal chemistry research.

Characterizing inhibitors of human AP endonuclease 1

AP endonuclease 1 (APE1) processes DNA lesions including apurinic/apyrimidinic sites and 3´-blocking groups, mediating base excision repair and single strand break repair. Much effort has focused on developing specific inhibitors of APE1, which could have important applications in basic research and potentially lead to clinical anticancer agents. We used structural, biophysical, and biochemical methods to characterize several reported inhibitors, including 7-nitroindole-2-carboxylic acid (CRT0044876), given its small size, reported potency, and widespread use for studying APE1. Intriguingly, NMR chemical shift perturbation (CSP) experiments show that CRT0044876 and three similar indole-2-carboxylic acids bind a pocket distal from the APE1 active site. A crystal structure confirms these findings and defines the pose for 5-nitroindole-2-carboxylic acid. However, dynamic light scattering experiments show the indole compounds form colloidal aggregates that could bind (sequester) APE1, causing nonspecific inhibition. Endonuclease assays show the compounds lack significant APE1 inhibition under conditions (detergent) that disrupt aggregation. Thus, binding of the indole-2-carboxylic acids at the remote pocket does not inhibit APE1 repair activity. Myricetin also forms aggregates and lacks APE1 inhibition under aggregate-disrupting conditions. Two other reported compounds (MLS000552981, MLS000419194) inhibit APE1 in vitro with low micromolar IC50 and do not appear to aggregate in this concentration range. However, NMR CSP experiments indicate the compounds do not bind specifically to apo- or Mg2+-bound APE1, pointing to a non-specific mode of inhibition, possibly DNA binding. Our results highlight methods for rigorous interrogation of putative APE1 inhibitors and should facilitate future efforts to discover compounds that specifically inhibit this important repair enzyme.

Structure-Based Identification of Naphthoquinones and Derivatives as Novel Inhibitors of Main Protease Mpro and Papain-like Protease PLpro of SARS-CoV-2

The worldwide COVID-19 pandemic caused by the coronavirus SARS-CoV-2 urgently demands novel direct antiviral treatments. The main protease (Mpro) and papain-like protease (PLpro) are attractive drug targets among coronaviruses due to their essential role in processing the polyproteins translated from the viral RNA. In this study, we virtually screened 688 naphthoquinoidal compounds and derivatives against Mpro of SARS-CoV-2. Twenty-four derivatives were selected and evaluated in biochemical assays against Mpro using a novel fluorogenic substrate. In parallel, these compounds were also assayed with SARS-CoV-2 PLpro. Four compounds inhibited Mpro with half-maximal inhibitory concentration (IC50) values between 0.41 μM and 9.0 μM. In addition, three compounds inhibited PLpro with IC50 ranging from 1.9 μM to 3.3 μM. To verify the specificity of Mpro and PLpro inhibitors, our experiments included an assessment of common causes of false positives such as aggregation, high compound fluorescence, and inhibition by enzyme oxidation. Altogether, we confirmed novel classes of specific Mpro and PLpro inhibitors. Molecular dynamics simulations suggest stable binding modes for Mpro inhibitors with frequent interactions with residues in the S1 and S2 pockets of the active site. For two PLpro inhibitors, interactions occur in the S3 and S4 pockets. In summary, our structure-based computational and biochemical approach identified novel naphthoquinonal scaffolds that can be further explored as SARS-CoV-2 antivirals.

DeepBLI: A Transferable Multichannel Model for Detecting β-Lactamase-Inhibitor Interaction

Pathogens producing β-lactamase pose a great challenge to antibiotic-resistant infection treatment; thus, it is urgent to discover novel β-lactamase inhibitors for drug development. Conventional high-throughput screening is very costly, and structure-based virtual screening is limited with mechanisms. In this study, we construct a novel multichannel deep neural network (DeepBLI) for β-lactamase inhibitor screening, pretrained with a label reversal KIBA data set and fine-tuned on β-lactamase-inhibitor pairs from BindingDB. First, the pairs of encoders (Conv and Att) fuse the information spatially and sequentially for both enzymes and inhibitors. Then, a co-attention module creates the connection between the inhibitor and enzyme embeddings. Finally, multichannel outputs fuse with an element-wise product and then are fed into 3-layer fully connected networks to predict interactions. Comparing the state-of-the-art methods, DeepBLI yields an AUROC of 0.9240 and an AUPRC of 0.9715, which indicates that it can identify new β-lactamase-inhibitor interactions. To demonstrate its prediction ability, an application of DeepBLI is described to screen potential inhibitor compounds for metallo-β-lactamase AIM-1 and repurpose rottlerin for four classes of β-lactamase targets, showing the possibility of being a broad-spectrum inhibitor. DeepBLI provides an effective way for antibacterial drug development, contributing to antibiotic-resistant therapeutics.

In Silico Identification of Multi-Target Ligands as Promising Hit Compounds for Neurodegenerative Diseases Drug Development

The conventional treatment of neurodegenerative diseases (NDDs) is based on the "one molecule-one target" paradigm. To combat the multifactorial nature of NDDs, the focus is now shifted toward the development of small-molecule-based compounds that can modulate more than one protein target, known as "multi-target-directed ligands" (MTDLs), while having low affinity for proteins that are irrelevant for the therapy. The in silico approaches have demonstrated a potential to be a suitable tool for the identification of MTDLs as promising drug candidates with reduction in cost and time for research and development. In this study more than 650,000 compounds were screened by a series of in silico approaches to identify drug-like compounds with predicted activity simultaneously towards three important proteins in the NDDs symptomatic treatment: acetylcholinesterase (AChE), histone deacetylase 2 (HDAC2), and monoamine oxidase B (MAO-B). The compounds with affinities below 5.0 µM for all studied targets were additionally filtered to remove known non-specifically binding or unstable compounds. The selected four hits underwent subsequent refinement through in silico blood-brain barrier penetration estimation, safety evaluation, and molecular dynamics simulations resulting in two hit compounds that constitute a rational basis for further development of multi-target active compounds against NDDs.

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.

On drug discovery against infectious diseases and academic medicinal chemistry contributions

This perspective is an attempt to document the problems that medicinal chemists are facing in drug discovery. It is also trying to identify relevant/possible, research areas in which academics can have an impact and should thus be the subject of grant calls. Accordingly, it describes how hit discovery happens, how compounds to be screened are selected from available chemicals and the possible reasons for the recurrent paucity of useful/exploitable results reported. This is followed by the successful hit to lead stories leading to recent and original antibacterials which are, or about to be, used in human medicine. Then, illustrated considerations and suggestions are made on the possible inputs of academic medicinal chemists. This starts with the observation that discovering a “good” hit in the course of a screening campaign still rely on a lot of luck – which is within the reach of academics –, that the hit to lead process requires a lot of chemistry and that if public–private partnerships can be important throughout these stages, they are absolute requirements for clinical trials. Concerning suggestions to improve the current hit success rate, one academic input in organic chemistry would be to identify new and pertinent chemical space, design synthetic accesses to reach these and prepare the corresponding chemical libraries. Concerning hit to lead programs on a given target, if no new hits are available, previously reported leads along with new structural data can be pertinent starting points to design, prepare and assay original analogues. In conclusion, this text is an actual plea illustrating that, in many countries, academic research in medicinal chemistry should be more funded, especially in the therapeutic area neglected by the industry. At the least, such funds would provide the intensive to secure series of hopefully relevant chemical entities which appears to often lack when considering the results of academic as well as industrial screening campaigns.

Systematic Analysis and Prediction of the Target Space of Bioactive Food Compounds: Filling the Chemobiological Gaps

Food compounds and their molecular interactions are crucial for health and provide new chemotypes and targets for drug and nutraceutic design. Here, we retrieve and analyze the complete set of published interactions of food compounds with human proteins using the FooDB as a compound set and ChEMBL as a source of interactions. The data are analyzed in terms of 19 target classes and 19 compound classes, showing a small fraction of target assignment for the compounds (1.6%) and unraveling multiple gaps in the chemobiological space for these molecules. By using well-established cheminformatic approaches [similarity ensemble approach (SEA) combined with the maximum Tanimoto coefficient to the nearest bioactive, "SEA + TC"], we achieve a much enhanced target assignment (64.2%), filling many of the gaps with target hypothesis for fast focused testing. By publishing these data sets and analyses, we expect to provide a set of resources to speed up the full clarification of the chemobiological space of food compounds, opening new opportunities for drug and nutraceutic design.

Enzyme Responsive Rigid-Rod Aromatics Target "Undruggable" Phosphatases to Kill Cancer Cells in a Mimetic Bone Microenvironment

Bone metastasis remains a challenge in cancer treatment. Here we show enzymatic responsive rigid-rod aromatics acting as the substrates of "undruggable" phosphatases to kill cancer cells in a mimetic bone microenvironment. By phosphorylation and conjugating nitrobenzoxadiazole (NBD) to hydroxybiphenylcarboxylate (BP), we obtained pBP-NBD (1P) as a substrate of both acid and alkaline phosphatases. 1P effectively kills both metastatic castration-resistant prostate cancer cells (mCRPCs) and osteoblast mimic cells in their coculture. 1P enters Saos2 almost instantly to target the endoplasmic reticulum (ER) of the cells. Co-culturing with Saos2 cells boosts the cellular uptake of 1P by mCRPCs. Cryo-EM reveals the nanotube structures of both 1P (2.4 Å resolution, pH 5.6) and 1 (2.2 Å resolution, pH 7.4). The helical packing of both nanotubes is identical, held together by strong pi-stacking interactions. Besides reporting the atomistic structure of nanotubes formed by the assembly of rigid-rod aromatics, this work expands the pool of molecules for designing EISA substrates that selectively target TME.

Boswellic acids, as novel inhibitor targeting peptidoglycan biosynthetic enzyme UDP-N-acetylglucosamine enolpyruvyl transferase (MurA) in Escherichia coli

UDP-N-acetylglucosamine enolpyruvyl transferase (MurA) is an essential cytosolic enzyme in the biosynthesis of peptidoglycan. It becomes a potential bacterial target for screening promising antibacterial compounds as it is associated with the early phases of peptidoglycan production. MurA enzyme is conserved and necessary for bacterial viability with no mammalian homolog, which is a well-proven therapeutic research target. The present study reports the natural compounds from Boswellia serrata targeting the MurA enzyme. The identified inhibitors against MurA Escherichia coli (E. coli): β-boswellic acid (IC₅₀ 33.65 µM), Acetyl-β-boswellic acid (IC₅₀ 30.17 µM), and Acetyl-11-keto-β-boswellic acid (IC₅₀ 37.67 µM). Inhibitors showed a fourfold decrease in IC₅₀ values on pre-incubation with substrate-UDP-N-acetyl-glucosamine (UDP-GlcNAc). Mode-of-inhibition studies revealed their uncompetitive nature with both the substrates. Although these boswellic acids have been explored for their pharmacological potential, this is the first study reporting these compounds' E. coli MurA inhibiting potential.

In Vitro and In Silico Antistaphylococcal Activity of Indole Alkaloids Isolated from Tabernaemontana cymosa Jacq (Apocynaceae)

The species of the genus Tabernaemontana have a long tradition of use in different pathologies of infectious origins; the antibacterial, antifungal, and antiviral effects related to the control of the pathologies where the species of this genus are used, have been attributed to the indole monoterpene alkaloids, mainly those of the iboga type. There are more than 1000 alkaloids isolated from different species of Tabernaemontana and other genera of the Apocynaceae family, several of which lack studies related to antibacterial activity. In the present study, four monoterpene indole alkaloids were isolated from the seeds of the species Tabernaemontana cymosa Jacq, namely voacangine (1), voacangine-7-hydroxyindolenine (2), 3-oxovoacangine (3), and rupicoline (4), which were tested in an in vitro antibacterial activity study against the bacteria S. aureus, sensitive and resistant to methicillin, and classified by the World Health Organization as critical for the investigation of new antibiotics. Of the four alkaloids tested, only voacangine was active against S. aureus, with an MIC of 50 µg/mL. In addition, an in silico study was carried out between the four isolated alkaloids and some proteins of this bacterium, finding that voacangine also showed binding to proteins involved in cell wall synthesis, mainly PBP2 and PBP2a.

Methylene Blue Is a Nonspecific Protein-Protein Interaction Inhibitor with Potential for Repurposing as an Antiviral for COVID-19

We have previously identified methylene blue, a tricyclic phenothiazine dye approved for clinical use for the treatment of methemoglobinemia and for other medical applications as a small-molecule inhibitor of the protein-protein interaction (PPI) between the spike protein of the SARS-CoV-2 coronavirus and ACE2, the first critical step of the attachment and entry of this coronavirus responsible for the COVID-19 pandemic. Here, we show that methylene blue concentration dependently inhibits this PPI for the spike protein of the original strain as well as for those of variants of concern such as the D614G mutant and delta (B.1.617.2) with IC50 in the low micromolar range (1-5 μM). Methylene blue also showed promiscuous activity and inhibited several other PPIs of viral proteins (e.g., HCoV-NL63-ACE2, hepatitis C virus E-CD81) as well as others (e.g., IL-2-IL-2Rα) with similar potency. This nonspecificity notwithstanding, methylene blue inhibited the entry of pseudoviruses bearing the spike protein of SARS-CoV-2 in hACE2-expressing host cells, both for the original strain and the delta variant. It also blocked SARS-CoV-2 (B.1.5) virus replication in Vero E6 cells with an IC50 in the low micromolar range (1.7 μM) when assayed using quantitative PCR of the viral RNA. Thus, while it seems to be a promiscuous PPI inhibitor with low micromolar activity and has a relatively narrow therapeutic index, methylene blue inhibits entry and replication of SARS-CoV-2, including several of its mutant variants, and has potential as a possible inexpensive, broad-spectrum, orally bioactive small-molecule antiviral for the prevention and treatment of COVID-19.

A Comprehensive Phenotypic Screening Strategy to Identify Modulators of Cargo Translocation by the Bacterial Type IVB Secretion System

Bacterial type IV secretion systems (T4SSs) are macromolecular machines that translocate effector proteins across multiple membranes into infected host cells. Loss of function mutations in genes encoding protein components of the T4SS render bacteria avirulent, highlighting the attractiveness of T4SSs as drug targets. Here, we designed an automated high-throughput screening approach for the identification of compounds that interfere with the delivery of a reporter-effector fusion protein from Legionella pneumophila into RAW264.7 mouse macrophages. Using a fluorescence resonance energy transfer (FRET)-based detection assay in a bacteria/macrophage coculture format, we screened a library of over 18,000 compounds and, upon vetting compound candidates in a variety of in vitro and cell-based secondary screens, isolated several hits that efficiently interfered with biological processes that depend on a functional T4SS, such as intracellular bacterial proliferation or lysosomal avoidance, but had no detectable effect on L. pneumophila growth in culture medium, conditions under which the T4SS is dispensable. Notably, the same hit compounds also attenuated, to varying degrees, effector delivery by the closely related T4SS from Coxiella burnetii, notably without impacting growth of this organism within synthetic media. Together, these results support the idea that interference with T4SS function is a possible therapeutic intervention strategy, and the emerging compounds provide tools to interrogate at a molecular level the regulation and dynamics of these virulence-critical translocation machines. IMPORTANCE Multi-drug-resistant pathogens are an emerging threat to human health. Because conventional antibiotics target not only the pathogen but also eradicate the beneficial microbiota, they often cause additional clinical complications. Thus, there is an urgent need for the development of "smarter" therapeutics that selectively target pathogens without affecting beneficial commensals. The bacterial type IV secretion system (T4SS) is essential for the virulence of a variety of pathogens but dispensable for bacterial viability in general and can, thus, be considered a pathogen's Achilles heel. By identifying small molecules that interfere with cargo delivery by the T4SS from two important human pathogens, Legionella pneumophila and Coxiella burnetii, our study represents the first step in our pursuit toward precision medicine by developing pathogen-selective therapeutics capable of treating the infections without causing harm to commensal bacteria.

The Discovery of Potent SHP2 Inhibitors with Anti-Proliferative Activity in Breast Cancer Cell Lines

Despite available treatments, breast cancer is the leading cause of cancer-related death. Knowing that the tyrosine phosphatase SHP2 is a regulator in tumorigenesis, developing inhibitors of SHP2 in breast cells is crucial. Our study investigated the effects of new compounds, purchased from NSC, on the phosphatase activity of SHP2 and the modulation of breast cancer cell lines’ proliferation and viability. A combined ligand-based and structure-based virtual screening protocol was validated, then performed, against SHP2 active site. Top ranked compounds were tested via SHP2 enzymatic assay, followed by measuring IC50 values. Subsequently, hits were tested for their anti-breast cancer viability and proliferative activity. Our experiments identified three compounds 13030, 24198, and 57774 as SHP2 inhibitors, with IC50 values in micromolar levels and considerable selectivity over the analogous enzyme SHP1. Long MD simulations of 500 ns showed a very promising binding mode in the SHP2 catalytic pocket. Furthermore, these compounds significantly reduced MCF-7 breast cancer cells’ proliferation and viability. Interestingly, two of our hits can have acridine or phenoxazine cyclic system known to intercalate in ds DNA. Therefore, our novel approach led to the discovery of SHP2 inhibitors, which could act as a starting point in the future for clinically useful anticancer agents.

Lockdown, a selective small-molecule inhibitor of the integrin phosphatase PPM1F, blocks cancer cell invasion

Phosphatase PPM1F is a regulator of cell adhesion by fine-tuning integrin activity and actin cytoskeleton structures. Elevated expression of this enzyme in human tumors is associated with high invasiveness, enhanced metastasis, and poor prognosis. Thus, PPM1F is a target for pharmacological intervention, yet inhibitors of this enzyme are lacking. Here, we use high-throughput screening to identify Lockdown, a reversible and non-competitive PPM1F inhibitor. Lockdown is selective for PPM1F, because this compound does not inhibit other protein phosphatases in vitro and does not induce additional phenotypes in PPM1F knockout cells. Importantly, Lockdown-treated glioblastoma cells fully re-capitulate the phenotype of PPM1F-deficient cells as assessed by increased phosphorylation of PPM1F substrates and corruption of integrin-dependent cellular processes. Ester modification yields Lockdown^Pro with increased membrane permeability and prodrug-like properties. Lockdown^Pro suppresses tissue invasion by PPM1F-overexpressing human cancer cells, validating PPM1F as a therapeutic target and providing an access point to control tumor cell dissemination.

Discovery of an Effective Small-Molecule Allosteric Inhibitor of New Delhi Metallo-β-lactamase (NDM)

To identify novel inhibitors of the carbapenemase New Delhi metallo-β-lactamase (NDM) as possible therapeutic compounds, we conducted a high-throughput screen of a 43,358-compound library. One of these compounds, a 2-quinazolinone linked through a diacylhydrazine to a phenyl ring (QDP-1) (IC₅₀ = 7.9 ± 0.5 μM), was characterized as a slow-binding reversible inhibitor (K_i^app = 4 ± 2 μM) with a noncompetitive mode of inhibition in which substrate and inhibitor enhance each other's binding affinity. These studies, along with differential scanning fluorimetry, zinc quantitation, and selectivity studies, support an allosteric mechanism of inhibition. Cotreatment with QDP-1 effectively lowers minimum inhibitory concentrations of carbapenems for a panel of resistant Escherichia coli and Klebsiella pneumoniae clinical isolates expressing NDM-1 but not for those expressing only serine carbapenemases. QDP-1 represents a novel allosteric approach for NDM drug development for potential use alone or with other NDM inhibitors to counter carbapenem resistance in enterobacterales.

Isometric Stratified Ensembles: A Partial and Incremental Adaptive Applicability Domain and Consensus-Based Classification Strategy for Highly Imbalanced Data Sets with Application to Colloidal Aggregation

Partial and incremental stratification analysis of a quantitative structure-interference relationship (QSIR) is a novel strategy intended to categorize classification provided by machine learning techniques. It is based on a 2D mapping of classification statistics onto two categorical axes: the degree of consensus and level of applicability domain. An internal cross-validation set allows to determine the statistical performance of the ensemble at every 2D map stratum and hence to define isometric local performance regions with the aim of better hit ranking and selection. During training, isometric stratified ensembles (ISE) applies a recursive decorrelated variable selection and considers the cardinal ratio of classes to balance training sets and thus avoid bias due to possible class imbalance. To exemplify the interest of this strategy, three different highly imbalanced PubChem pairs of AmpC β-lactamase and cruzain inhibition assay campaigns of colloidal aggregators and complementary aggregators data set available at the AGGREGATOR ADVISOR predictor web page were employed. Statistics obtained using this new strategy show outperforming results compared to former published tools, with and without a classical applicability domain. ISE performance on classifying colloidal aggregators shows from a global AUC of 0.82, when the whole test data set is considered, up to a maximum AUC of 0.88, when its highest confidence isometric stratum is retained.

Backbone-Anchoring, Solid-Phase Synthesis Strategy To Access a Library of Peptidouridine-Containing Small Molecules

Nucleoside diphosphate sugar (NDP-sugar) substrates provide the inspiration for nucleoside analogue inhibitor scaffolds. By employing solid-phase synthesis, we provide a method to access a library of peptidouridine inhibitors with both minimal compound handling and purification steps. Specifically, this strategy is exemplified by generating uridine diphosphate sugar (UDP-sugar) mimics, which allow for compound elaboration by altering the dipeptide composition, the N-terminal linkage, and a pendant aryl group. To exemplify the versatility, 41 unique nucleoside analogues are presented.

Hexokinase 2 Inhibition and Biological Effects of BNBZ and Its Derivatives: The Influence of the Number and Arrangement of Hydroxyl Groups

Hexokinase 2 (HK2), an enzyme of the sugar kinase family, plays a dual role in glucose metabolism and mediating cancer cell apoptosis, making it an attractive target for cancer therapy. While positive HK2 expression usually promotes cancer cells survival, silencing or inhibiting this enzyme has been found to improve the effectiveness of anti-cancer drugs and even result in cancer cell death. Previously, benitrobenrazide (BNBZ) was characterized as a potent HK2 inhibitor with good anti-cancer activity in mice, but the effect of its trihydroxy moiety (pyrogallol-like) on inhibitory activity and some cellular functions has not been fully understood. Therefore, the main goal of this study was to obtain the parent BNBZ (2a) and its three dihydroxy derivatives 2b–2d and to conduct additional physicochemical and biological investigations. The research hypothesis assumed that the HK2 inhibitory activity of the tested compounds depends on the number and location of hydroxyl groups in their chemical structure. Among many studies, the binding affinity to HK2 was determined and two human liver cancer cell lines, HepG2 and HUH7, were used and exposed to chemicals at various times: 24 h, 48 h and 72 h. The study showed that the modifications to the structures of the new BNBZ derivatives led to significant changes in their activities. It was also found that these compounds tend to aggregate and exhibit toxic effects. They were found to contribute to: (a) DNA damage, (b) increased ROS production, and (c) disruption of cell cycle progression. It was observed that, HepG2, occurred much more sensitive to the tested chemicals than the HUH7 cells; However, regardless of the used cell line it seems that the increase in the expression of HK2 in cancer cells compared to normal cells which have HK2 at a very low level, is a serious obstacle in anti-cancer therapy and efforts to find the effective inhibitors of this enzyme should be intensified.

Tuning the rate of aggregation of hIAPP into amyloid using small-molecule modulators of assembly

Human islet amyloid polypeptide (hIAPP) self-assembles into amyloid fibrils which deposit in pancreatic islets of type 2 diabetes (T2D) patients. Here, we applied chemical kinetics to study the mechanism of amyloid assembly of wild-type hIAPP and its more amyloidogenic natural variant S20G. We show that the aggregation of both peptides involves primary nucleation, secondary nucleation and elongation. We also report the discovery of two structurally distinct small-molecule modulators of hIAPP assembly, one delaying the aggregation of wt hIAPP, but not S20G; while the other enhances the rate of aggregation of both variants at substoichiometric concentrations. Investigation into the inhibition mechanism(s) using chemical kinetics, native mass spectrometry, fluorescence titration, SPR and NMR revealed that the inhibitor retards primary nucleation, secondary nucleation and elongation, by binding peptide monomers. By contrast, the accelerator predominantly interacts with species formed in the lag phase. These compounds represent useful chemical tools to study hIAPP aggregation and may serve as promising starting-points for the development of therapeutics for T2D.

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.

Reduction of Promiscuous Peptides-Enzyme Inhibition and Aggregation by Negatively Charged Biopolymers

In this work, peptides selected from a microarray were found to inhibit β-gal with promiscuous mechanisms. Peptides inhibited the enzyme in a noncompetitive kinetics, and the inhibition of enzyme activities was reduced under high enzyme concentrations and the addition of detergent. Dynamic light scattering and atomic force microscope revealed that peptide/enzyme aggregation was related to inhibited enzyme activities. Positively charged residues of arginine and lysine were critical for the enzyme inhibition. The preincubation of peptide inhibitors with negatively charged biopolymers of polyphosphates, ssDNA, and low pI peptides could increase the residual activity of peptide-inhibited enzyme, possibly due to the disruption of the electrostatic interaction between positively charged peptide residues and the β-gal surface. Further, negative biopolymers were able to recover the activity of the aggregated peptide/β-gal complex. Negatively charged biopolymers could be used in high-throughput screening assays to reduce peptides/protein aggregation and thereby minimize promiscuous inhibitions.

Structure-based identification of naphthoquinones and derivatives as novel inhibitors of main protease Mpro and papain-like protease PLpro of SARS-CoV-2

The worldwide COVID-19 pandemic caused by the coronavirus SARS-CoV-2 urgently demands novel direct antiviral treatments. The main protease (Mpro) and papain-like protease (PLpro) are attractive drug targets among coronaviruses due to their essential role in processing the polyproteins translated from the viral RNA. In the present work, we virtually screened 688 naphthoquinoidal compounds and derivatives against Mpro of SARS-CoV-2. Twenty-four derivatives were selected and evaluated in biochemical assays against Mpro using a novel fluorogenic substrate. In parallel, these compounds were also assayed with SARS-CoV-2 PLpro. Four compounds inhibited Mpro with half-maximal inhibitory concentration (IC ₅₀ ) values between 0.41 µM and 66 µM. In addition, eight compounds inhibited PLpro with IC ₅₀ ranging from 1.7 µM to 46 µM. Molecular dynamics simulations suggest stable binding modes for Mpro inhibitors with frequent interactions with residues in the S1 and S2 pockets of the active site. For two PLpro inhibitors, interactions occur in the S3 and S4 pockets. In summary, our structure-based computational and biochemical approach identified novel naphthoquinonal scaffolds that can be further explored as SARS-CoV-2 antivirals.

Fundamental considerations in drug design

The drug discovery paradigm has been very time-consuming, challenging, and expensive; however, the disease conditions originating from bacteria, virus, protozoa, fungus and other microorganisms are steadily shooting up. For instance, COVID-19 is the latest viral infection that affects millions of people and the world’s economy very severely. Therefore, the quest for discovery of novel and potent drug compounds against deadly pathogens is crucial at the moment. Despite a lot of drawbacks in drug discovery and development and its pertaining technology, the advancement must be taken into account so the time duration and cost would be minimized. In this chapter, basic principles in drug design and discovery have been discussed together with advances in drug development.

Discovery of Highly Potent Fusion Inhibitors with Potential Pan-Coronavirus Activity That Effectively Inhibit Major COVID-19 Variants of Concern (VOCs) in Pseudovirus-Based Assays

We report the discovery of several highly potent small molecules with low-nM potency against severe acute respiratory syndrome coronavirus (SARS-CoV; lowest half-maximal inhibitory concentration (IC₅₀: 13 nM), SARS-CoV-2 (IC₅₀: 23 nM), and Middle East respiratory syndrome coronavirus (MERS-CoV; IC₅₀: 76 nM) in pseudovirus-based assays with excellent selectivity index (SI) values (>5000), demonstrating potential pan-coronavirus inhibitory activities. Some compounds showed 100% inhibition against the cytopathic effects (CPE; IC₁₀₀) of an authentic SARS-CoV-2 (US_WA-1/2020) variant at 1.25 µM. The most active inhibitors also potently inhibited variants of concern (VOCs), including the UK (B.1.1.7) and South African (B.1.351) variants and the Delta variant (B.1.617.2) originally identified in India in pseudovirus-based assay. Surface plasmon resonance (SPR) analysis with one potent inhibitor confirmed that it binds to the prefusion SARS-CoV-2 spike protein trimer. These small-molecule inhibitors prevented virus-mediated cell-cell fusion. The absorption, distribution, metabolism, and excretion (ADME) data for one of the most active inhibitors, NBCoV1, demonstrated drug-like properties. An in vivo pharmacokinetics (PK) study of NBCoV1 in rats demonstrated an excellent half-life (t_1/2) of 11.3 h, a mean resident time (MRT) of 14.2 h, and oral bioavailability. We expect these lead inhibitors to facilitate the further development of preclinical and clinical candidates.

Updated Prediction of Aggregators and Assay-Interfering Substructures in Food Compounds

Positive outcomes in biochemical and biological assays of food compounds may appear due to the well-described capacity of some compounds to form colloidal aggregates that adsorb proteins, resulting in their denaturation and loss of function. This phenomenon can lead to wrongly ascribing mechanisms of biological action for these compounds (false positives) as the effect is nonspecific and promiscuous. Similar false positives can show up due to chemical (photo)reactivity, redox cycling, metal chelation, interferences with the assay technology, membrane disruption, etc., which are more frequently observed when the tested molecule has some definite interfering substructures. Although discarding false positives can be achieved experimentally, it would be very useful to have in advance a prognostic value for possible aggregation and/or interference based only in the chemical structure of the compound tested in order to be aware of possible issues, help in prioritization of compounds to test, design of appropriate assays, etc. Previously, we applied cheminformatic tools derived from the drug discovery field to identify putative aggregators and interfering substructures in a database of food compounds, the FooDB, comprising 26,457 molecules at that time. Here, we provide an updated account of that analysis based on a current, much-expanded version of the FooDB, comprising a total of 70,855 compounds. In addition, we also apply a novel machine learning model (SCAM Detective) to predict aggregators with 46-53% increased accuracies over previous models. In this way, we expect to provide the researchers in the mode of action of food compounds with a much improved, robust, and widened set of putative aggregators and interfering substructures of food compounds.