Small-molecule targeting PKM2 provides a molecular basis of lactylation-dependent fibroblast-like synoviocytes proliferation inhibition against rheumatoid arthritis

Fibroblast-like synoviocytes (FLS) play an important role in rheumatoid arthritis (RA)-related swelling and bone damage. Therefore, novel targets for RA therapy in FLS are urgently discovered for improving pathologic phenomenon, especially joint damage and dyskinesia. Here, we suggested that pyruvate kinase M2 (PKM2) in FLS represented a pharmacological target for RA treatment by antimalarial drug artemisinin (ART). We demonstrated that ART selectively inhibited human RA-FLS and rat collagen-induced arthritis (CIA)-FLS proliferation and migration without observed toxic effects. In particular, the identification of targets revealed that PKM2 played a crucial role as a primary regulator of the cell cycle, leading to the heightened proliferation of RA-FLS. ART exhibited a direct interaction with PKM2, resulting in an allosteric modulation that enhances the lactylation modification of PKM2. This interaction further promoted the binding of p300, ultimately preventing the nuclear translocation of PKM2 and inducing cell cycle arrest at the S phase. In vivo, ART obviously suppressed RA-mediated synovial hyperplasia, bone damage and inflammatory response to further improve motor behavior in CIA-rats. Taken together, these findings indicate that directing interventions towards PKM2 in FLS could offer a hopeful avenue for pharmaceutical treatments of RA through the regulation of cell cycle via PKM2 lactylation.

Development of small-molecular-based radiotracers for PET imaging of PD-L1 expression and guiding the PD-L1 therapeutics

PURPOSE

Programmed cell death protein ligand 1 (PD-L1) is a crucial biomarker for immunotherapy. However, nearly 70% of patients do not respond to PD-L1 immune checkpoint therapy. Accurate monitoring of PD-L1 expression and quantification of target binding during treatment are essential. In this study, a series of small-molecule radiotracers were developed to assess PD-L1 expression and direct immunotherapy.

METHODS

Radiotracers of [68Ga]Ga-D-PMED, [68Ga]Ga-D-PEG-PMED, and [68Ga]Ga-D-pep-PMED were designed based on a 2-methyl-3-biphenyl methanol scaffold and successfully synthesized. Cellular experiments and molecular docking assays were performed to determine their specificity for PD-L1. PD-L1 status was investigated via positron emission tomography (PET) imaging in MC38 tumor models. PET imaging of [68Ga]Ga-D-pep-PMED was performed to noninvasively quantify PD-L1 blocking using an anti-mouse PD-L1 antibody (PD-L1 mAb).

RESULTS

The radiosyntheses of [68Ga]Ga-D-PMED, [68Ga]Ga-D-PEG-PMED, and [68Ga]Ga-D-pep-PMED were achieved with radiochemical yields of 87 ± 6%, 82 ± 4%, and 79 ± 9%, respectively. In vitro competition assays demonstrated their high affinities (the IC50 values of [68Ga]Ga-D-PMED, [68Ga]Ga-D-PEG-PMED, and [68Ga]Ga-D-pep-PMED were 90.66 ± 1.24, 160.8 ± 1.35, and 51.6 ± 1.32 nM, respectively). At 120 min postinjection (p.i.) of the radiotracers, MC38 tumors displayed optimized tumor-to-muscle ratios for all radioligands. Owing to its hydrophilic modification, [68Ga]Ga-D-pep-PMED had the highest target-to-nontarget (T/NT) ratio of approximately 6.2 ± 1.2. Interestingly, the tumor/liver ratio was hardly affected by different concentrations of the inhibitor BMS202. We then evaluated the impacts of dose and time on accessible PD-L1 levels in the tumor during anti-mouse PD-L1 antibody treatment. The tumor uptake of [68Ga]Ga-D-pep-PMED significantly decreased with increasing PD-L1 mAb dose. Moreover, after 8 days of treatment with a single antibody, the uptake of [68Ga]Ga-D-pep-PMED in the tumor significantly increased but remained lower than that in the saline group.

CONCLUSION

PET imaging with [68Ga]Ga-D-pep-PMED, a small-molecule radiotracer, is a promising tool for evaluating PD-L1 expression and quantifying the target blockade of PD-L1 to assist in the development of effective therapeutic regimens.

Allosteric Modulation of the YAP/TAZ-TEAD Interaction by Palmitoylation and Small-Molecule Inhibitors

The Hippo signaling pathway is a highly conserved signaling network that plays a central role in regulating cellular growth, proliferation, and organ size. This pathway consists of a kinase cascade that integrates various upstream signals to control the activation or inactivation of YAP/TAZ proteins. Phosphorylated YAP/TAZ is sequestered in the cytoplasm; however, when the Hippo pathway is deactivated, it translocates into the nucleus, where it associates with TEAD transcription factors. This partnership is instrumental in regulating the transcription of progrowth and antiapoptotic genes. Thus, in many cancers, aberrantly hyperactivated YAP/TAZ promotes oncogenesis by contributing to cancer cell proliferation, metastasis, and therapy resistance. Because YAP and TAZ exert their oncogenic effects by binding with TEAD, it is critical to understand this key interaction to develop cancer therapeutics. Previous research has indicated that TEAD undergoes autopalmitoylation at a conserved cysteine, and small molecules that inhibit TEAD palmitoylation disrupt effective YAP/TAZ binding. However, how exactly palmitoylation contributes to YAP/TAZ-TEAD interactions and how the TEAD palmitoylation inhibitors disrupt this interaction remains unknown. Utilizing molecular dynamics simulations, our investigation not only provides detailed atomistic insight into the YAP/TAZ-TEAD dynamics but also unveils that the inhibitor studied influences the binding of YAP and TAZ to TEAD in distinct manners. This discovery has significant implications for the design and deployment of future molecular interventions targeting this interaction.

Mitochondria-Selective Dicationic Small-Molecule Ligand Targeting G-Quadruplex Structures for Human Colorectal Cancer Therapy

Mitochondria are important drug targets for anticancer and other disease therapies. Certain human mitochondrial DNA sequences capable of forming G-quadruplex structures (G4s) are emerging drug targets of small molecules. Despite some mitochondria-selective ligands being reported for drug delivery against cancers, the ligand design is mostly limited to the triphenylphosphonium scaffold. The ligand designed with lipophilic small-sized scaffolds bearing multipositive charges targeting the unique feature of high mitochondrial membrane potential (MMP) is lacking and most mitochondria-selective ligands are not G4-targeting. Herein, we report a new small-sized dicationic lipophilic ligand to target MMP and mitochondrial DNA G4s to enhance drug delivery for anticancer. The ligand showed marked alteration of mitochondrial gene expression and substantial induction of ROS production, mitochondrial dysfunction, DNA damage, cellular senescence, and apoptosis. The ligand also exhibited high anticancer activity against HCT116 cancer cells (IC50, 3.4 μM) and high antitumor efficacy in the HCT116 tumor xenograft mouse model (∼70% tumor weight reduction).

Discovery of Small Molecule Interleukin 17A Inhibitors with Novel Binding Mode and Stoichiometry: Optimization of DNA-Encoded Chemical Library Hits to In Vivo Active Compounds

Dysregulation of IL17A drives numerous inflammatory and autoimmune disorders with inhibition of IL17A using antibodies proven as an effective treatment. Oral anti-IL17 therapies are an attractive alternative option, and several preclinical small molecule IL17 inhibitors have previously been described. Herein, we report the discovery of a novel class of small molecule IL17A inhibitors, identified via a DNA-encoded chemical library screen, and their subsequent optimization to provide in vivo efficacious inhibitors. These new protein-protein interaction (PPI) inhibitors bind in a previously undescribed mode in the IL17A protein with two copies binding symmetrically to the central cavities of the IL17A homodimer.

Cell Painting-based bioactivity prediction boosts high-throughput screening hit-rates and compound diversity

Identifying active compounds for a target is a time- and resource-intensive task in early drug discovery. Accurate bioactivity prediction using morphological profiles could streamline the process, enabling smaller, more focused compound screens. We investigate the potential of deep learning on unrefined single-concentration activity readouts and Cell Painting data, to predict compound activity across 140 diverse assays. We observe an average ROC-AUC of 0.744 ± 0.108 with 62% of assays achieving ≥0.7, 30% ≥0.8, and 7% ≥0.9. In many cases, the high prediction performance can be achieved using only brightfield images instead of multichannel fluorescence images. A comprehensive analysis shows that Cell Painting-based bioactivity prediction is robust across assay types, technologies, and target classes, with cell-based assays and kinase targets being particularly well-suited for prediction. Experimental validation confirms the enrichment of active compounds. Our findings indicate that models trained on Cell Painting data, combined with a small set of single-concentration data points, can reliably predict the activity of a compound library across diverse targets and assays while maintaining high hit rates and scaffold diversity. This approach has the potential to reduce the size of screening campaigns, saving time and resources, and enabling primary screening with more complex assays.

Visualized Tracking and Multidimensional Assessing of Mitochondria-Associated Pyroptosis in Cancer Cells by a Small-Molecule Fluorescent Probe

Pyroptosis is closely related to the development and treatment of various cancers; thus, comprehensive studies of the correlations between pyroptosis and its inductive or inhibitive factors can provide new ideas for the intervention and diagnosis of tumors. The dysfunction of mitochondria may induce pyroptosis in cancer cells, which can be reflected by the fluctuations of the microenvironmental parameters in mitochondria as well as the changes of mitochondrial DNA level and morphology, etc. To precisely track and assess the mitochondria-associated pyroptosis process, simultaneous visualization of changes in multiphysiological parameters in mitochondria is highly desirable. In this work, we reported a nonreaction-based, multifunctional small-molecule fluorescent probe Mito-DK with the capability of crosstalk-free response to polarity and mtDNA as well as mitochondrial morphology. Accurate assessment of mitochondria-associated pyroptosis induced by palmitic acid/H2O2 was achieved through monitoring changes in mitochondrial multiple parameters with the help of Mito-DK. In particular, the pyroptosis-inducing ability of an antibiotic doxorubicin and the pyroptosis-inhibiting capacity of an anticancer agent puerarin were evaluated by Mito-DK. These results provide new perspectives for visualizing mitochondria-associated pyroptosis and offer new approaches for screening pyroptosis-related anticancer agents.

DeepP450: Predicting Human P450 Activities of Small Molecules by Integrating Pretrained Protein Language Model and Molecular Representation

Cytochrome P450 enzymes (CYPs) play a crucial role in Phase I drug metabolism in the human body, and CYP activity toward compounds can significantly affect druggability, making early prediction of CYP activity and substrate identification essential for therapeutic development. Here, we established a deep learning model for assessing potential CYP substrates, DeepP450, by fine-tuning protein and molecule pretrained models through feature integration with cross-attention and self-attention layers. This model exhibited high prediction accuracy (0.92) on the test set, with area under the receiver operating characteristic curve (AUROC) values ranging from 0.89 to 0.98 in substrate/nonsubstrate predictions across the nine major human CYPs, surpassing current benchmarks for CYP activity prediction. Notably, DeepP450 uses only one model to predict substrates/nonsubstrates for any of the nine CYPs and exhibits certain generalizability on novel compounds and different categories of human CYPs, which could greatly facilitate early stage drug design by avoiding CYP-reactive compounds.

Virtual Screening of a Chemically Diverse "Superscaffold" Library Enables Ligand Discovery for a Key GPCR Target

The advent of ultra-large libraries of drug-like compounds has significantly broadened the possibilities in structure-based virtual screening, accelerating the discovery and optimization of high-quality lead chemotypes for diverse clinical targets. Compared to traditional high-throughput screening, which is constrained to libraries of approximately one million compounds, the ultra-large virtual screening approach offers substantial advantages in both cost and time efficiency. By expanding the chemical space with compounds synthesized from easily accessible and reproducible reactions and utilizing a large, diverse set of building blocks, we can enhance both the diversity and quality of the discovered lead chemotypes. In this study, we explore new chemical spaces using reactions of sulfur(VI) fluorides to create a combinatorial library consisting of several hundred million compounds. We screened this virtual library for cannabinoid type II receptor (CB2) antagonists using the high-resolution structure in conjunction with a rationally designed antagonist, AM10257. The top-predicted compounds were then synthesized and tested in vitro for CB2 binding and functional antagonism, achieving an experimentally validated hit rate of 55%. Our findings demonstrate the effectiveness of reliable reactions, such as sulfur fluoride exchange, in diversifying ultra-large chemical spaces and facilitate the discovery of new lead compounds for important biological targets.

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.

Proteome-Based Investigation Identified Potential Drug Repurposable Small Molecules Against Monkeypox Disease

Monkeypox Virus (MPXV), the causative agent of Monkeypox (MPX) disease, is an emerging zoonotic pathogen spreading in different endemic and non-endemic nations and creating outbreaks. MPX treatment mainly includes Cidofovir and Tecovirimat but they have several side effects and solely depending on these drugs may promote the emergence of drug-resistant variants. Hence, new drugs are required to control the spread of the disease. In this study, we explored the MPXV proteome to suggest repurposable drugs. DrugBank screening revealed drugs such as Brinzolamide, Dorzolamide, Methazolamide, Zidovudine, Gemcitabine, Hydroxyurea, Fludarabine, and Tecovirimat as controls. Structural analogs of these compounds were extracted from ChEMBL Database. After Molecular docking and Absorption, Distribution, Metabolism, Excretion, and Toxicity (ADMET)-based screening, we identified Zidovudine (binding affinity-5.9 kcal/mol) and a Harmala alkaloid (2S,4R)-4-(9H-Pyrido[3,4-b]indol-1-yl)-1,2,4-butanetriol (binding affinity - 6.6 kcal/mol) against L2R receptor (Thymidine Kinase). Moreover, Fludarabine (binding affinity - 6.4 kcal/mol) and 5'-Dehydroadenosine (binding affinity - 6.4 kcal/mol) can strongly interact with the I4L receptor (Ribonucleotide reductase large subunit R1). Molecular Dynamics (MD) simulations suggest all of these compounds can change the C-alpha backbone, residue mobility, compactness, and solvent accessible surface area of L2R and I4L. Our results strongly suggest that these drug repurposing small molecules are worth exploring in vivo and in vitro for clinical applications.

High-throughput screening of small-molecules libraries identified antibacterials against clinically relevant multidrug-resistant A. baumannii and K. pneumoniae

BACKGROUND

The current pipeline for new antibiotics fails to fully address the significant threat posed by drug-resistant Gram-negative bacteria that have been identified by the World Health Organization (WHO) as a global health priority. New antibacterials acting through novel mechanisms of action are urgently needed. We aimed to identify new chemical entities (NCEs) with activity against Klebsiella pneumoniae and Acinetobacter baumannii that could be developed into a new treatment for drug-resistant infections.

METHODS

We developed a high-throughput phenotypic screen and selection cascade for generation of hit compounds active against multidrug-resistant (MDR) strains of K. pneumoniae and A. baumannii. We screened compound libraries selected from the proprietary collections of three pharmaceutical companies that had exited antibacterial drug discovery but continued to accumulate new compounds to their collection. Compounds from two out of three libraries were selected using "eNTRy rules" criteria associated with increased likelihood of intracellular accumulation in Escherichia coli.

FINDINGS

We identified 72 compounds with confirmed activity against K. pneumoniae and/or drug-resistant A. baumannii. Two new chemical series with activity against XDR A. baumannii were identified meeting our criteria of potency (EC50 ≤50 μM) and absence of cytotoxicity (HepG2 CC50 ≥100 μM and red blood cell lysis HC50 ≥100 μM). The activity of close analogues of the two chemical series was also determined against A. baumannii clinical isolates.

INTERPRETATION

This work provides proof of principle for the screening strategy developed to identify NCEs with antibacterial activity against multidrug-resistant critical priority pathogens such as K. pneumoniae and A. baumannii. The screening and hit selection cascade established here provide an excellent foundation for further screening of new compound libraries to identify high quality starting points for new antibacterial lead generation projects.

FUNDING

BMBF and GARDP.

High-Throughput Methods for the Discovery of Small Molecule Modulators of Pancreatic Beta-Cell Function and Regeneration

The progression of type II diabetes (T2D) is characterized by a complex and highly variable loss of beta-cell mass, resulting in impaired insulin secretion. Many T2D drug discovery efforts aimed at discovering molecules that can protect or restore beta-cell mass and function have been developed using limited beta-cell lines and primary rodent/human pancreatic islets. Various high-throughput screening methods have been used in the context of drug discovery, including luciferase-based reporter assays, glucose-stimulated insulin secretion, and high-content screening. In this context, a cornerstone of small molecule discovery has been the use of immortalized rodent beta-cell lines. Although insightful, this usage has led to a more comprehensive understanding of rodent beta-cell proliferation pathways rather than their human counterparts. Advantages gained in enhanced physiological relevance are offered by three-dimensional (3D) primary islets and pseudoislets in contrast to monolayer cultures, but these approaches have been limited to use in low-throughput experiments. Emerging methods, such as high-throughput 3D islet imaging coupled with machine learning, aim to increase the feasibility of integrating 3D microtissue structures into high-throughput screening. This review explores the current methods used in high-throughput screening for small molecule modulators of beta-cell mass and function, a potentially pivotal strategy for diabetes drug discovery.

Recent applications of artificial intelligence in RNA-targeted small molecule drug discovery

INTRODUCTION

Targeting RNAs with small molecules offers an alternative to the conventional protein-targeted drug discovery and can potentially address unmet and emerging medical needs. The recent rise of interest in the strategy has already resulted in large amounts of data on disease associated RNAs, as well as on small molecules that bind to such RNAs. Artificial intelligence (AI) approaches, including machine learning and deep learning, present an opportunity to speed up the discovery of RNA-targeted small molecules by improving decision-making efficiency and quality.

AREAS COVERED

The topics described in this review include the recent applications of AI in the identification of RNA targets, RNA structure determination, screening of chemical compound libraries, and hit-to-lead optimization. The impact and limitations of the recent AI applications are discussed, along with an outlook on the possible applications of next-generation AI tools for the discovery of novel RNA-targeted small molecule drugs.

EXPERT OPINION

Key areas for improvement include developing AI tools for understanding RNA dynamics and RNA - small molecule interactions. High-quality and comprehensive data still need to be generated especially on the biological activity of small molecules that target RNAs.

Scaffold Hopping and Screening for Potent Small Molecule Agonists for GRP94: Implications to Alleviate ER Stress-Associated Pathogenesis

Disparity in the activity of Endoplasmic reticulum (ER) leads to degenerative diseases, mainly associated with protein misfolding and aggregation leading to cellular dysfunction and damage, ultimately contributing to ER stress. ER stress activates the complex network of Unfolded Protein Response (UPR) signaling pathways mediated by transmembrane proteins IRE1, ATF6, and PERK. In addition to UPR, many ER chaperones have evolved to optimize the output of properly folded secretory and membrane proteins. Glucose-regulated protein 94 (GRP94), an ER chaperone of heat shock protein HSP90 family, directs protein folding through interaction with other components of the ER protein folding machinery and assists in ER-associated degradation (ERAD). Activation of GRP94 would increase the efficacy of protein folding machinery and regulate the UPR pathway toward homeostasis. The present study aims to screen for novel agonists for GRP94 based on Core hopping, pharmacophore hypothesis, 3D-QSAR, and virtual screening with small-molecule compound libraries in order to improve the efficiency of native protein folding by enhancing GRP94 chaperone activity, therefore to reduce protein misfolding and aggregation. In this study, we have employed the strategy of small molecule-dependent ER programming to enhance the chaperone activity of GRP94 through scaffold hopping-based screening approach to identify specific GRP94 agonists. New scaffolds generated by altering the cores of NECA, the known GRP94 agonist, were validated by employing pharmacophore hypothesis testing, 3D-QSAR modeling, and molecular dynamics simulations. This facilitated the identification of small molecules to improve the efficiency of native protein folding by enhancing GRP94 activity. High-throughput virtual screening of the selected pharmacophore hypothesis against Selleckchem and ZINC databases retrieved a total of 2,27,081 compounds. Further analysis on docking and ADMET properties revealed Epimedin A, Narcissoside, Eriocitrin 1,2,3,4,6-O-Pentagalloylglucose, Secoisolariciresinol diglucoside, ZINC92952357, ZINC67650204, and ZINC72457930 as potential lead molecules. The stability and interaction of these small molecules were far better than the known agonist, NECA indicating their efficacy in selectively alleviating ER stress-associated pathogenesis. These results substantiate the fact that small molecule-dependent ER reprogramming would activate the ER chaperones and therefore reduce the protein misfolding as well as aggregation associated with ER stress in order to restore cellular homeostasis.

Large-Scale Pretraining Improves Sample Efficiency of Active Learning-Based Virtual Screening

Virtual screening of large compound libraries to identify potential hit candidates is one of the earliest steps in drug discovery. As the size of commercially available compound collections grows exponentially to the scale of billions, active learning and Bayesian optimization have recently been proven as effective methods of narrowing down the search space. An essential component of those methods is a surrogate machine learning model that predicts the desired properties of compounds. An accurate model can achieve high sample efficiency by finding hits with only a fraction of the entire library being virtually screened. In this study, we examined the performance of a pretrained transformer-based language model and graph neural network in a Bayesian optimization active learning framework. The best pretrained model identifies 58.97% of the top-50,000 compounds after screening only 0.6% of an ultralarge library containing 99.5 million compounds, improving 8% over the previous state-of-the-art baseline. Through extensive benchmarks, we show that the superior performance of pretrained models persists in both structure-based and ligand-based drug discovery. Pretrained models can serve as a boost to the accuracy and sample efficiency of active learning-based virtual screening.

Consensus Pharmacophore Strategy For Identifying Novel SARS-Cov-2 Mpro Inhibitors from Large Chemical Libraries

The severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) main Protease (Mpro) is an enzyme that cleaves viral polyproteins translated from the viral genome and is critical for viral replication. Mpro is a target for anti-SARS-CoV-2 drug development, and multiple Mpro crystals complexed with competitive inhibitors have been reported. In this study, we aimed to develop an Mpro consensus pharmacophore as a tool to expand the search for inhibitors. We generated a consensus model by aligning and summarizing pharmacophoric points from 152 bioactive conformers of SARS-CoV-2 Mpro inhibitors. Validation against a library of conformers from a subset of ligands showed that our model retrieved poses that reproduced the crystal-binding mode in 77% of the cases. Using models derived from a consensus pharmacophore, we screened >340 million compounds. Pharmacophore-matching and chemoinformatics analyses identified new potential Mpro inhibitors. The candidate compounds were chemically dissimilar to the reference set, and among them, demonstrating the relevance of our model. We evaluated the effect of 16 candidates on Mpro enzymatic activity finding that seven have inhibitory activity. Three compounds (1, 4, and 5) had IC50 values in the midmicromolar range. The Mpro consensus pharmacophore reported herein can be used to identify compounds with improved activity and novel chemical scaffolds against Mpro. The method developed for its generation is provided as an open-access code (https://github.com/AngelRuizMoreno/ConcensusPharmacophore) and can be applied to other pharmacological targets.

Using Generative Modeling to Endow with Potency Initially Inert Compounds with Good Bioavailability and Low Toxicity

In the early stages of drug development, large chemical libraries are typically screened to identify compounds of promising potency against the chosen targets. Often, however, the resulting hit compounds tend to have poor drug metabolism and pharmacokinetics (DMPK), with negative developability features that may be difficult to eliminate. Therefore, starting the drug discovery process with a "null library", compounds that have highly desirable DMPK properties but no potency against the chosen targets, could be advantageous. Here, we explore the opportunities offered by machine learning to realize this strategy in the case of the inhibition of α-synuclein aggregation, a process associated with Parkinson's disease. We apply MolDQN, a generative machine learning method, to build an inhibitory activity against α-synuclein aggregation into an initial inactive compound with good DMPK properties. Our results illustrate how generative modeling can be used to endow initially inert compounds with desirable developability properties.

Multiplexed experimental strategies for fragment library screening against challenging drug targets using SPR biosensors

Surface plasmon resonance (SPR) biosensor methods are ideally suited for fragment-based lead discovery.  However, generally applicable experimental procedures and detailed protocols are lacking, especially for structurally or physico-chemically challenging targets or when tool compounds are not available. Success depends on accounting for the features of both the target and the chemical library, purposely designing screening experiments for identification and validation of hits with desired specificity and mode-of-action, and availability of orthogonal methods capable of confirming fragment hits. The range of targets and libraries amenable to an SPR biosensor-based approach for identifying hits is considerably expanded by adopting multiplexed strategies, using multiple complementary surfaces or experimental conditions. Here we illustrate principles and multiplexed approaches for using flow-based SPR biosensor systems for screening fragment libraries of different sizes (90 and 1056 compounds) against a selection of challenging targets. It shows strategies for the identification of fragments interacting with 1) large and structurally dynamic targets, represented by acetyl choline binding protein (AChBP), a Cys-loop receptor ligand gated ion channel homologue, 2) targets in multi protein complexes, represented by lysine demethylase 1 and a corepressor (LSD1/CoREST), 3) structurally variable or unstable targets, represented by farnesyl pyrophosphate synthase (FPPS), 4) targets containing intrinsically disordered regions, represented by protein tyrosine phosphatase 1B  (PTP1B), and 5) aggregation-prone proteins, represented by an engineered form of human tau  (tau K18M). Practical considerations and procedures accounting for the characteristics of the proteins and libraries, and that increase robustness, sensitivity, throughput and versatility are highlighted. The study shows that the challenges for addressing these types of targets is not identification of potentially useful fragments per se, but establishing methods for their validation and evolution into leads.

Discovery of a First-in-Class Small-Molecule Ligand for WDR91 Using DNA-Encoded Chemical Library Selection Followed by Machine Learning

WD40 repeat-containing protein 91 (WDR91) regulates early-to-late endosome conversion and plays vital roles in endosome fusion, recycling, and transport. WDR91 was recently identified as a potential host factor for viral infection. We employed DNA-encoded chemical library (DEL) selection against the WDR domain of WDR91, followed by machine learning to predict ligands from the synthetically accessible Enamine REAL database. Screening of predicted compounds identified a WDR91 selective compound 1, with a KD of 6 ± 2 μM by surface plasmon resonance. The co-crystal structure confirmed the binding of 1 to the WDR91 side pocket, in proximity to cysteine 487, which led to the discovery of covalent analogues 18 and 19. The covalent adduct formation for 18 and 19 was confirmed by intact mass liquid chromatography-mass spectrometry. The discovery of 1, 18, and 19, accompanying structure-activity relationship, and the co-crystal structures provide valuable insights for designing potent and selective chemical tools against WDR91 to evaluate its therapeutic potential.

Identification of novel plant cysteine oxidase inhibitors from a yeast chemical genetic screen

Hypoxic responses in plants involve Plant Cysteine Oxidases (PCOs). They catalyze the N-terminal cysteine oxidation of Ethylene Response Factors VII (ERF-VII) in an oxygen-dependent manner, leading to their degradation via the cysteine N-degron pathway (Cys-NDP) in normoxia. In hypoxia, PCO activity drops, leading to the stabilization of ERF-VIIs and subsequent hypoxic gene upregulation. Thus far, no chemicals have been described to specifically inhibit PCO enzymes. In this work, we devised an in vivo pipeline to discover Cys-NDP effector molecules. Budding yeast expressing AtPCO4 and plant-based ERF-VII reporters was deployed to screen a library of natural-like chemical scaffolds and was further combined with an Arabidopsis Cys-NDP reporter line. This strategy allowed us to identify three PCO inhibitors, two of which were shown to affect PCO activity in vitro. Application of these molecules to Arabidopsis seedlings led to an increase in ERF-VII stability, induction of anaerobic gene expression, and improvement of tolerance to anoxia. By combining a high-throughput heterologous platform and the plant model Arabidopsis, our synthetic pipeline provides a versatile system to study how the Cys-NDP is modulated. Its first application here led to the discovery of at least two hypoxia-mimicking molecules with the potential to impact plant tolerance to low oxygen stress.

Drug discovery for heart failure targeting myosin-binding protein C

Cardiac MyBP-C (cMyBP-C) interacts with actin and myosin to fine-tune cardiac muscle contractility. Phosphorylation of cMyBP-C, which reduces the binding of cMyBP-C to actin and myosin, is often decreased in patients with heart failure (HF) and is cardioprotective in model systems of HF. Therefore, cMyBP-C is a potential target for HF drugs that mimic its phosphorylation and/or perturb its interactions with actin or myosin. We labeled actin with fluorescein-5-maleimide (FMAL) and the C0-C2 fragment of cMyBP-C (cC0-C2) with tetramethylrhodamine (TMR). We performed two complementary high-throughput screens (HTS) on an FDA-approved drug library, to discover small molecules that specifically bind to cMyBP-C and affect its interactions with actin or myosin, using fluorescence lifetime (FLT) detection. We first excited FMAL and detected its FLT, to measure changes in fluorescence resonance energy transfer (FRET) from FMAL (donor) to TMR (acceptor), indicating binding. Using the same samples, we then excited TMR directly, using a longer wavelength laser, to detect the effects of compounds on the environmentally sensitive FLT of TMR, to identify compounds that bind directly to cC0-C2. Secondary assays, performed on selected modulators with the most promising effects in the primary HTS assays, characterized the specificity of these compounds for phosphorylated versus unphosphorylated cC0-C2 and for cC0-C2 versus C1-C2 of fast skeletal muscle (fC1-C2). A subset of identified compounds modulated ATPase activity in cardiac and/or skeletal myofibrils. These assays establish the feasibility of the discovery of small-molecule modulators of the cMyBP-C-actin/myosin interaction, with the ultimate goal of developing therapies for HF.

Graph neural networks for the identification of novel inhibitors of a small RNA

MicroRNAs (miRNAs) play a crucial role in post-transcriptional gene regulation and have been implicated in various diseases, including cancers and lung disease. In recent years, Graph Neural Networks (GNNs) have emerged as powerful tools for analyzing graph-structured data, making them well-suited for the analysis of molecular structures. In this work, we explore the application of GNNs in ligand-based drug screening for small molecules targeting miR-21. By representing a known dataset of small molecules targeting miR-21 as graphs, GNNs can learn complex relationships between their structures and activities, enabling the prediction of potential miRNA-targeting small molecules by capturing the structural features and similarity between known miRNA-targeting compounds. The use of GNNs in miRNA-targeting drug screening holds promise for the discovery of novel therapeutic agents and provides a computational framework for efficient screening of large chemical libraries.

ABPP-CoDEL: Activity-Based Proteome Profiling-Guided Discovery of Tyrosine-Targeting Covalent Inhibitors from DNA-Encoded Libraries

DNA-encoded chemical library (DEL) has been extensively used for lead compound discovery for decades in academia and industry. Incorporating an electrophile warhead into DNA-encoded compounds recently permitted the discovery of covalent ligands that selectively react with a particular cysteine residue. However, noncysteine residues remain underexplored as modification sites of covalent DELs. Herein, we report the design and utility of tyrosine-targeting DELs of 67 million compounds. Proteome-wide reactivity analysis of tyrosine-reactive sulfonyl fluoride (SF) covalent probes suggested three enzymes (phosphoglycerate mutase 1, glutathione s-transferase 1, and dipeptidyl peptidase 3) as models of tyrosine-targetable proteins. Enrichment with SF-functionalized DELs led to the identification of a series of tyrosine-targeting covalent inhibitors of the model enzymes. In-depth mechanistic investigation revealed their novel modes of action and reactive ligand-accessible hotspots of the enzymes. Our strategy of combining activity-based proteome profiling and covalent DEL enrichment (ABPP-CoDEL), which generated selective covalent binders against a variety of target proteins, illustrates the potential use of this methodology in further covalent drug discovery.

Covalent fragment libraries in drug discovery-Design, synthesis, and screening methods

As the development of drugs with a covalent mode of action is becoming increasingly popular, well-validated covalent fragment-based drug discovery (FBDD) methods have been comparatively slow to keep up with the demand. In this chapter the principles of covalent fragment reactivity, library design, synthesis, and screening methods are explored in depth, focussing on literature examples with direct applications to practical covalent fragment library design and screening. Further, questions about the future of the field are explored and potential useful advances are proposed.

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.

Novel inhibitors that target bacterial virulence identified via HTS against intra-macrophage survival of Shigella flexneri

Shigella flexneri is a facultative intracellular pathogen that causes shigellosis, a human diarrheal disease characterized by the destruction of the colonic epithelium. Novel antimicrobial compounds to treat infections are urgently needed due to the proliferation of bacterial antibiotic resistance and lack of new effective antimicrobials in the market. Our approach to find compounds that block the Shigella virulence pathway has three potential advantages: (i) resistance development should be minimized due to the lack of growth selection pressure, (ii) no resistance due to environmental antibiotic exposure should be developed since the virulence pathways are not activated outside of host infection, and (iii) the normal intestinal microbiota, which do not have the targeted virulence pathways, should be unharmed. We chose to utilize two phenotypic assays, inhibition of Shigella survival in macrophages and Shigella growth inhibition (minimum inhibitory concentration), to interrogate the 1.7 M compound screening collection subset of the GlaxoSmithKline drug discovery chemical library. A number of secondary assays on the hit compounds resulting from the primary screens were conducted, which, in combination with chemical, structural, and physical property analyses, narrowed the final hit list to 44 promising compounds for further drug discovery efforts. The rapid development of antibiotic resistance is a critical problem that has the potential of returning the world to a "pre-antibiotic" type of environment, where millions of people will die from previously treatable infections. One relatively newer approach to minimize the selection pressures for the development of resistance is to target virulence pathways. This is anticipated to eliminate any resistance selection pressure in environmental exposure to virulence-targeted antibiotics and will have the added benefit of not affecting the non-virulent microbiome. This paper describes the development and application of a simple, reproducible, and sensitive assay to interrogate an extensive chemical library in high-throughput screening format for activity against the survival of Shigella flexneri 2457T-nl in THP-1 macrophages. The ability to screen very large numbers of compounds in a reasonable time frame (~1.7 M compounds in ~8 months) distinguishes this assay as a powerful tool in further exploring new compounds with intracellular effect on S. flexneri or other pathogens with similar pathways of pathogenesis. The assay utilizes a luciferase reporter which is extremely rapid, simple, relatively inexpensive, and sensitive and possesses a broad linear range. The assay also utilized THP-1 cells that resemble primary monocytes and macrophages in morphology and differentiation properties. THP-1 cells have advantages over human primary monocytes or macrophages because they are highly plastic and their homogeneous genetic background minimizes the degree of variability in the cell phenotype (1). The intracellular and virulence-targeted selectivity of our methodology, determined via secondary screening, is an enormous advantage. Our main interest focuses on hits that are targeting virulence, and the most promising compounds with adequate physicochemical and drug metabolism and pharmacokinetic (DMPK) properties will be progressed to a suitable in vivo shigellosis model to evaluate the therapeutic potential of this approach. Additionally, compounds that act via a host-directed mechanism could be a promising source for further research given that it would allow a whole new, specific, and controlled approach to the treatment of diseases caused by some pathogenic bacteria.

Machine Learning-Boosted Docking Enables the Efficient Structure-Based Virtual Screening of Giga-Scale Enumerated Chemical Libraries

The emergence of ultra-large screening libraries, filled to the brim with billions of readily available compounds, poses a growing challenge for docking-based virtual screening. Machine learning (ML)-boosted strategies like the tool HASTEN combine rapid ML prediction with the brute-force docking of small fractions of such libraries to increase screening throughput and take on giga-scale libraries. In our case study of an anti-bacterial chaperone and an anti-viral kinase, we first generated a brute-force docking baseline for 1.56 billion compounds in the Enamine REAL lead-like library with the fast Glide high-throughput virtual screening protocol. With HASTEN, we observed robust recall of 90% of the true 1000 top-scoring virtual hits in both targets when docking only 1% of the entire library. This reduction of the required docking experiments by 99% significantly shortens the screening time. In the kinase target, the employment of a hydrogen bonding constraint resulted in a major proportion of unsuccessful docking attempts and hampered ML predictions. We demonstrate the optimization potential in the treatment of failed compounds when performing ML-boosted screening and benchmark and showcase HASTEN as a fast and robust tool in a growing arsenal of approaches to unlock the chemical space covered by giga-scale screening libraries for everyday drug discovery campaigns.

Screening in serum-derived medium reveals differential response to compounds targeting metabolism

A challenge for screening new anticancer drugs is that efficacy in cell culture models is not always predictive of efficacy in patients. One limitation of standard cell culture is a reliance on non-physiological nutrient levels, which can influence cell metabolism and drug sensitivity. A general assessment of how physiological nutrients affect cancer cell response to small molecule therapies is lacking. To address this, we developed a serum-derived culture medium that supports the proliferation of diverse cancer cell lines and is amenable to high-throughput screening. We screened several small molecule libraries and found that compounds targeting metabolic enzymes were differentially effective in standard compared to serum-derived medium. We exploited the differences in nutrient levels between each medium to understand why medium conditions affected the response of cells to some compounds, illustrating how this approach can be used to screen potential therapeutics and understand how their efficacy is modified by available nutrients.

Autophagy inhibitors for cancer therapy: Small molecules and nanomedicines

Autophagy is a conserved process in which the cytosolic materials are degraded and eventually recycled for cellular metabolism to maintain homeostasis. The dichotomous role of autophagy in pathogenesis is complicated. Accumulating reports have suggested that cytoprotective autophagy is responsible for tumor growth and progression. Autophagy inhibitors, such as chloroquine (CQ) and hydroxychloroquine (HCQ), are promising for treating malignancies or overcoming drug resistance in chemotherapy. With the rapid development of nanotechnology, nanomaterials also show autophagy-inhibitory effects or are reported as the carriers delivering autophagy inhibitors. In this review, we summarize the small-molecule compounds and nanomaterials inhibiting autophagic flux as well as the mechanisms involved. The nanocarrier-based drug delivery systems for autophagy inhibitors and their distinct advantages are also described. The progress of autophagy inhibitors for clinical applications is finally introduced, and their future perspectives are discussed.

Small-molecule discovery through DNA-encoded libraries

The development of bioactive small molecules as probes or drug candidates requires discovery platforms that enable access to chemical diversity and can quickly reveal new ligands for a target of interest. Within the past 15 years, DNA-encoded library (DEL) technology has matured into a widely used platform for small-molecule discovery, yielding a wide variety of bioactive ligands for many therapeutically relevant targets. DELs offer many advantages compared with traditional screening methods, including efficiency of screening, easily multiplexed targets and library selections, minimized resources needed to evaluate an entire DEL and large library sizes. This Review provides accounts of recently described small molecules discovered from DELs, including their initial identification, optimization and validation of biological properties including suitability for clinical applications.

AI-Powered Virtual Screening of Large Compound Libraries Leads to the Discovery of Novel Inhibitors of Sirtuin-1

The discovery of new scaffolds and chemotypes via high-throughput screening is tedious and resource intensive. Yet, there are millions of small molecules commercially available, rendering comprehensive in vitro tests intractable. We show how smart algorithms reduce large screening collections to target-specific sets of just a few hundred small molecules, allowing for a much faster and more cost-effective hit discovery process. We showcase the application of this virtual screening strategy by preselecting 434 compounds for Sirtuin-1 inhibition from a library of 2.6 million compounds, corresponding to 0.02% of the original library. Multistage in vitro validation ultimately confirmed nine chemically novel inhibitors. When compared to a competitive benchmark study for Sirtuin-1, our method shows a 12-fold higher hit rate. The results demonstrate how AI-driven preselection from large screening libraries allows for a massive reduction in the number of small molecules to be tested in vitro while still retaining a large number of hits.

Protein-protein interactions: developing small-molecule inhibitors/stabilizers through covalent strategies

The development of small-molecule inhibitors or stabilizers of selected protein-protein interactions (PPIs) of interest holds considerable promise for the development of research tools as well as candidate therapeutics. In this context, the covalent modification of selected residues within the target protein has emerged as a promising mechanism of action to obtain small-molecule modulators of PPIs with appropriate selectivity and duration of action. Different covalent labeling strategies are now available that can potentially allow for a rational, ground-up discovery and optimization of ligands as PPI inhibitors or stabilizers. This review article provides a synopsis of recent developments and applications of such tactics, with a particular focus on site-directed fragment tethering and proximity-enabled approaches.

Targeting RNA Structure to Inhibit Editing in Trypanosomes

Mitochondrial RNA editing in trypanosomes represents an attractive target for developing safer and more efficient drugs for treating infections with trypanosomes because this RNA editing pathway is not found in humans. Other workers have targeted several enzymes in this editing system, but not the RNA. Here, we target a universal domain of the RNA editing substrate, which is the U-helix formed between the oligo-U tail of the guide RNA and the target mRNA. We selected a part of the U-helix that is rich in G-U wobble base pairs as the target site for the virtual screening of 262,000 compounds. After chemoinformatic filtering of the top 5000 leads, we subjected 50 representative complexes to 50 nanoseconds of molecular dynamics simulations. We identified 15 compounds that retained stable interactions in the deep groove of the U-helix. The microscale thermophoresis binding experiments on these five compounds show low-micromolar to nanomolar binding affinities. The UV melting studies show an increase in the melting temperatures of the U-helix upon binding by each compound. These five compounds can serve as leads for drug development and as research tools to probe the role of the RNA structure in trypanosomal RNA editing.

Characterisation of IL-23 receptor antagonists and disease relevant mutants using fluorescent probes

Association of single nucleotide polymorphisms in the IL-23 receptor with several auto-inflammatory diseases, led to the heterodimeric receptor and its cytokine-ligand IL-23, becoming important drug targets. Successful antibody-based therapies directed against the cytokine have been licenced and a class of small peptide antagonists of the receptor have entered clinical trials. These peptide antagonists may offer therapeutic advantages over existing anti-IL-23 therapies, but little is known about their molecular pharmacology. In this study, we use a fluorescent version of IL-23 to characterise antagonists of the full-length receptor expressed by living cells using a NanoBRET competition assay. We then develop a cyclic peptide fluorescent probe, specific to the IL23p19:IL23R interface and use this molecule to characterise further receptor antagonists. Finally, we use the assays to study the immunocompromising C115Y IL23R mutation, demonstrating that the mechanism of action is a disruption of the binding epitope for IL23p19.

Structure-based discovery of conformationally selective inhibitors of the serotonin transporter

The serotonin transporter (SERT) removes synaptic serotonin and is the target of anti-depressant drugs. SERT adopts three conformations: outward-open, occluded, and inward-open. All known inhibitors target the outward-open state except ibogaine, which has unusual anti-depressant and substance-withdrawal effects, and stabilizes the inward-open conformation. Unfortunately, ibogaine's promiscuity and cardiotoxicity limit the understanding of inward-open state ligands. We docked over 200 million small molecules against the inward-open state of the SERT. Thirty-six top-ranking compounds were synthesized, and thirteen inhibited; further structure-based optimization led to the selection of two potent (low nanomolar) inhibitors. These stabilized an outward-closed state of the SERT with little activity against common off-targets. A cryo-EM structure of one of these bound to the SERT confirmed the predicted geometry. In mouse behavioral assays, both compounds had anxiolytic- and anti-depressant-like activity, with potencies up to 200-fold better than fluoxetine (Prozac), and one substantially reversed morphine withdrawal effects.

Innovations in targeting RNA by fragment-based ligand discovery

A subset of functional regions within large RNAs fold into complex structures able to bind small-molecule ligands with high affinity and specificity. Fragment-based ligand discovery (FBLD) offers notable opportunities for discovery and design of potent small molecules that bind pockets in RNA. Here we share an integrated analysis of recent innovations in FBLD, emphasizing opportunities resulting from fragment elaboration via both linking and growing. Analysis of elaborated fragments emphasizes that high-quality interactions form with complex tertiary structures in RNA. FBLD-inspired small molecules have been shown to modulate RNA functions by competitively inhibiting protein binding and by selectively stabilizing dynamic RNA states. FBLD is creating a foundation to interrogate the relatively unknown structural space for RNA ligands and for discovery of RNA-targeted therapeutics.

Discovery of the 3-Amino-1,2,4-triazine-Based Library as Selective PDK1 Inhibitors with Therapeutic Potential in Highly Aggressive Pancreatic Ductal Adenocarcinoma

Pyruvate dehydrogenase kinases (PDKs) are serine/threonine kinases, that are directly involved in altered cancer cell metabolism, resulting in cancer aggressiveness and resistance. Dichloroacetic acid (DCA) is the first PDK inhibitor that has entered phase II clinical; however, several side effects associated with weak anticancer activity and excessive drug dose (100 mg/kg) have led to its limitation in clinical application. Building upon a molecular hybridization approach, a small library of 3-amino-1,2,4-triazine derivatives has been designed, synthesized, and characterized for their PDK inhibitory activity using in silico, in vitro, and in vivo assays. Biochemical screenings showed that all synthesized compounds are potent and subtype-selective inhibitors of PDK. Accordingly, molecular modeling studies revealed that a lot of ligands can be properly placed inside the ATP-binding site of PDK1. Interestingly, 2D and 3D cell studies revealed their ability to induce cancer cell death at low micromolar doses, being extremely effective against human pancreatic KRAS mutated cancer cells. Cellular mechanistic studies confirm their ability to hamper the PDK/PDH axis, thus leading to metabolic/redox cellular impairment, and to ultimately trigger apoptotic cancer cell death. Remarkably, preliminary in vivo studies performed on a highly aggressive and metastatic Kras-mutant solid tumor model confirm the ability of the most representative compound 5i to target the PDH/PDK axis in vivo and highlighted its equal efficacy and better tolerability profile with respect to those elicited by the reference FDA approved drugs, cisplatin and gemcitabine. Collectively, the data highlights the promising anticancer potential of these novel PDK-targeting derivatives toward obtaining clinical candidates for combatting highly aggressive KRAS-mutant pancreatic ductal adenocarcinomas.

ChemFlow─From 2D Chemical Libraries to Protein-Ligand Binding Free Energies

The accurate prediction of protein-ligand binding affinities is a fundamental problem for the rational design of new drug entities. Current computational approaches are either too expensive or inaccurate to be effectively used in virtual high-throughput screening campaigns. In addition, the most sophisticated methods, e.g., those based on configurational sampling by molecular dynamics, require significant pre- and postprocessing to provide a final ranking, which hinders straightforward applications by nonexpert users. We present a novel computational platform named ChemFlow to bridge the gap between 2D chemical libraries and estimated protein-ligand binding affinities. The software is designed to prepare a library of compounds provided in SMILES or SDF format, dock them into the protein binding site, and rescore the poses by simplified free energy calculations. Using a data set of 626 protein-ligand complexes and GPU computing, we demonstrate that ChemFlow provides relative binding free energies with an RMSE < 2 kcal/mol at a rate of 1000 ligands per day on a midsize computer cluster. The software is publicly available at https://github.com/IFMlab/ChemFlow.

3D Spheroid Invasion Assay for High-Throughput Screening of Small-Molecule Libraries

Cancer metastasis is a complex cascade that involves the activation of cancer cell migration and invasion of the extracellular space. Cancer-associated fibroblasts (CAFs) are known inducers of cancer cell invasion. However, current in vitro invasion assays such as the Boyden chamber assay are cumbersome and low throughput. Therefore, there is an urgent need for new ex vivo, surrogate invasion assays that can faithfully recapitulate the cancer cell invasion process in vitro and are amenable to large-scale screening of small-molecule libraries in a high-throughput fashion. Here, we describe a well-established high-throughput three-dimensional (3D) spheroid invasion assay as a powerful tool to identify novel molecular targets that can potentially mediate CAF-dependent cancer cell invasion.

Exploring Allosteric Inhibitors of Protein Tyrosine Phosphatases Through High-Throughput Screening

High-throughput screening (HTS) using a natural or synthetic chemical or natural product library is a powerful technique for discovering novel small-molecular-weight compounds in order to develop drugs that specifically inhibit or activate molecular targets, malfunctioning of which underlies the development of diseases, especially malignant neoplasms. In contrast to a large number of successful cases in obtaining inhibitors against protein tyrosine kinases (PTKs) using HTS, however, the development of selective inhibitors for protein tyrosine phosphatases (PTPs) has lagged since PTP family members share highly conserved catalytic domain structures. Here, in this chapter we describe a novel method for exploring seed compounds of allosteric PTP inhibitors from a chemical/natural product library through HTS.

Development of Optimal Virtual Screening Strategies to Identify Novel Toll-Like Receptor Ligands Using the DockBox Suite

Toll-like receptors (TLRs) represent attractive targets for developing modulators for the treatment of many pathologies, including inflammation, cancer, and autoimmune diseases. Here, we describe a protocol based on the DockBox package that enables to set up and perform structure-based virtual screening in order to increase the chance of identifying novel TLR ligands from chemical libraries.

Small Molecular Inhibitors Reverse Cancer Metastasis by Blockading Oncogenic PITPNM3

Most cancer-related deaths are a result of metastasis. The development of small molecular inhibitors reversing cancer metastasis represents a promising therapeutic opportunity for cancer patients. This pan-cancer analysis identifies oncogenic roles of membrane-associated phosphatidylinositol transfer protein 3 (PITPNM3), which is crucial for cancer metastasis. Small molecules targeting PITPNM3 must be explored further. Here, PITPNM3-selective small molecular inhibitors are reported. These compounds exhibit target-specific inhibition of PITPNM3 signaling, thereby reducing metastasis of breast cancer cells. Besides, by using nanoparticle-based delivery systems, these PITPNM3-selective compounds loaded nanoparticles significantly repress metastasis of breast cancer in mouse xenograft models and organoid models. Notably, the results establish an important metastatic-promoting role for PITPNM3 and offer PITPNM3 inhibition as a therapeutic strategy in metastatic breast cancer.

First studies on tumor associated carbonic anhydrases IX and XII monoclonal antibodies conjugated to small molecule inhibitors

We report for the first time Antibody-Drug-Conjugates (ADCs) containing human (h) Carbonic Anhydrase (CA; EC 4.2.1.1) directed Monoclonal Antibodies (MAbs) linked to low molecular weight inhibitors of the same enzymes by means of hydrophilic peptide spacers. In agreement with the incorporated CA directed MAb fragments, in vitro inhibition data of the obtained ADCs showed sub-nanomolar KI values for the tumour associated CAs IX and XII which were up to 10-fold more potent when compared to the corresponding unconjugated MAbs. In addition, the introduction of the CA inhibitor (CAI) benzenesulfonamide allowed the ADCs to potently inhibit the housekeeping tumoral off-target human CA II isoform. Such results are supporting the definition of an unprecedented reported class of ADCs able to hit simultaneously multiple hCAs physiologically cooperative in maintaining altered cellular metabolic pathways, and therefore ideal for the treatment of chronic diseases such as cancers and inflammation diseases.

Low-Molecular Weight Small Molecules Can Potently Bind RNA and Affect Oncogenic Pathways in Cells

RNA is challenging to target with bioactive small molecules, particularly those of low molecular weight that bind with sufficient affinity and specificity. In this report, we developed a platform to address this challenge, affording a novel bioactive interaction. An RNA-focused small-molecule fragment collection (n = 2500) was constructed by analyzing features in all publicly reported compounds that bind RNA, the largest collection of RNA-focused fragments to date. The RNA-binding landscape for each fragment was studied by using a library-versus-library selection with an RNA library displaying a discrete structural element, probing over 12.8 million interactions, the greatest number of interactions between fragments and biomolecules probed experimentally. Mining of this dataset across the human transcriptome defined a drug-like fragment that potently and specifically targeted the microRNA-372 hairpin precursor, inhibiting its processing into the mature, functional microRNA and alleviating invasive and proliferative oncogenic phenotypes in gastric cancer cells. Importantly, this fragment has favorable properties, including an affinity for the RNA target of 300 ± 130 nM, a molecular weight of 273 Da, and quantitative estimate of drug-likeness (QED) score of 0.8. (For comparison, the mean QED of oral medicines is 0.6 ± 0.2). Thus, these studies demonstrate that a low-molecular weight, fragment-like compound can specifically and potently modulate RNA targets.

A machine learning model trained on a high-throughput antibacterial screen increases the hit rate of drug discovery

Screening for novel antibacterial compounds in small molecule libraries has a low success rate. We applied machine learning (ML)-based virtual screening for antibacterial activity and evaluated its predictive power by experimental validation. We first binarized 29,537 compounds according to their growth inhibitory activity (hit rate 0.87%) against the antibiotic-resistant bacterium Burkholderia cenocepacia and described their molecular features with a directed-message passing neural network (D-MPNN). Then, we used the data to train an ML model that achieved a receiver operating characteristic (ROC) score of 0.823 on the test set. Finally, we predicted antibacterial activity in virtual libraries corresponding to 1,614 compounds from the Food and Drug Administration (FDA)-approved list and 224,205 natural products. Hit rates of 26% and 12%, respectively, were obtained when we tested the top-ranked predicted compounds for growth inhibitory activity against B. cenocepacia, which represents at least a 14-fold increase from the previous hit rate. In addition, more than 51% of the predicted antibacterial natural compounds inhibited ESKAPE pathogens showing that predictions expand beyond the organism-specific dataset to a broad range of bacteria. Overall, the developed ML approach can be used for compound prioritization before screening, increasing the typical hit rate of drug discovery.

Invertebrate neurones, genomes, phenotypic and target-based screening; their contributions to the search for new chemical leads and new molecular targets for the control of pests, parasites and disease vectors

Insect-borne diseases of humans, animals and plants can be devastating. The direct damage to crops by insect and nematode pests can also severely reduce crop yields and threaten harvests. Parasitic nematodes can impair human health and the health of farm livestock. Effective control for all such pests, vectors and pathogens is required as the economic and health burden can be substantial. Insecticides, nematicides and anthelmintics have been at the forefront of control and will remain important in the immediate future, even as we explore new and more sustainable methods to maintain the necessary disease control and the growth in food supply. Many important chemicals deployed for the control of invertebrate disease vectors and pathogens of humans, agricultural crops and farm livestock are active on ion channels, resulting in rapid actions. Understanding their modes of action has been accelerated by studies on the physiology of identifiable invertebrate excitable cells. Nematode and insect genetic model organisms and comparative genomics have contributed to defining the molecular targets of insecticides and anthelmintics, facilitating target-based screening. Automated phenotyping, which allows high-throughput screening of chemical libraries for new and re-purposed compounds, has been increasingly deployed in the search for new molecules of interest. With a growing world population to be fed and a 20-49% loss of global harvest to pests, we need to maintain control of the pests, parasites and pathogens that threaten global food supply and global health.

A BRET Ca2+ sensor enables high-throughput screening in the presence of background fluorescence

The intrinsic fluorescence of samples confounds the use of fluorescence-based sensors. This is of particular concern in high-throughput screening (HTS) applications using large chemical libraries containing intrinsically fluorescent compounds. To overcome this problem, we developed a bioluminescence resonance energy transfer (BRET) Ca2+ sensor, CalfluxCTN. We demonstrated that it reliably reported changes in intracellular Ca2+ concentrations evoked by an agonist and an antagonist of the human muscarinic acetylcholine receptor M1 (hM1R) even in the presence of the fluorescent compound fluorescein, which interfered with a standard fluorescent HTS sensor (Fluo-8). In an HTS using a chemical library containing fluorescent compounds, CalfluxCTN accurately identified agonists and antagonists that were missed or miscategorized using Fluo-8. Moreover, we showed that a luciferase substrate that becomes activated only when inside cells generated long-lasting BRET signals in HTS, enabling results to be reliably compared among replicate samples for hours. Thus, the use of a self-luminescent sensor instead of a fluorescent sensor could facilitate the complete screening of chemical libraries in a high-throughput context and enable analysis of autofluorescent samples in many different applications.

Targeting MuRF1 by small molecules in a HFpEF rat model improves myocardial diastolic function and skeletal muscle contractility

BACKGROUND

About half of heart failure (HF) patients, while having preserved left ventricular function, suffer from diastolic dysfunction (so-called HFpEF). No specific therapeutics are available for HFpEF in contrast to HF where reduced ejection fractions (HFrEF) can be treated pharmacologically. Myocardial titin filament stiffening, endothelial dysfunction, and skeletal muscle (SKM) myopathy are suspected to contribute to HFpEF genesis. We previously described small molecules interfering with MuRF1 target recognition thereby attenuating SKM myopathy and dysfunction in HFrEF animal models. The aim of the present study was to test the efficacy of one small molecule (MyoMed-205) in HFpEF and to describe molecular changes elicited by MyoMed-205.

METHODS

Twenty-week-old female obese ZSF1 rats received the MuRF1 inhibitor MyoMed-205 for 12 weeks; a comparison was made to age-matched untreated ZSF1-lean (healthy) and obese rats as controls. LV (left ventricle) function was assessed by echocardiography and by invasive haemodynamic measurements until week 32. At week 32, SKM and endothelial functions were measured and tissues collected for molecular analyses. Proteome-wide analysis followed by WBs and RT-PCR was applied to identify specific genes and affected molecular pathways. MuRF1 knockout mice (MuRF1-KO) SKM tissues were included to validate MuRF1-specificity.

RESULTS

By week 32, untreated obese rats had normal LV ejection fraction but augmented E/e' ratios and increased end diastolic pressure and myocardial fibrosis, all typical features of HFpEF. Furthermore, SKM myopathy (both atrophy and force loss) and endothelial dysfunction were detected. In contrast, MyoMed-205 treated rats had markedly improved diastolic function, less myocardial fibrosis, reduced SKM myopathy, and increased SKM function. SKM extracts from MyoMed-205 treated rats had reduced MuRF1 content and lowered total muscle protein ubiquitination. In addition, proteomic profiling identified eight proteins to respond specifically to MyoMed-205 treatment. Five out of these eight proteins are involved in mitochondrial metabolism, dynamics, or autophagy. Consistent with the mitochondria being a MyoMed-205 target, the synthesis of mitochondrial respiratory chain complexes I + II was increased in treated rats. MuRF1-KO SKM controls also had elevated mitochondrial complex I and II activities, also suggesting mitochondrial activity regulation by MuRF1.

CONCLUSIONS

MyoMed-205 improved myocardial diastolic function and prevented SKM atrophy/function in the ZSF1 animal model of HFpEF. Mechanistically, SKM benefited from an attenuated ubiquitin proteasome system and augmented synthesis/activity of proteins of the mitochondrial respiratory chain while the myocardium seemed to benefit from reduced titin modifications and fibrosis.