Seven Year Itch: Pan-Assay Interference Compounds (PAINS) in 2017-Utility and Limitations

Synergizing Chemical Structures and Bioassay Descriptions for Enhanced Molecular Property Prediction in Drug Discovery

The precise prediction of molecular properties can greatly accelerate the development of new drugs. However, in silico molecular property prediction approaches have been limited so far to assays for which large amounts of data are available. In this study, we develop a new computational approach leveraging both the textual description of the assay of interest and the chemical structure of target compounds. By combining these two sources of information via self-supervised learning, our tool can provide accurate predictions for assays where no measurements are available. Remarkably, our approach achieves state-of-the-art performance on the FS-Mol benchmark for zero-shot prediction, outperforming a wide variety of deep learning approaches. Additionally, we demonstrate how our tool can be used for tailoring screening libraries for the assay of interest, showing promising performance in a retrospective case study on a high-throughput screening campaign. By accelerating the early identification of active molecules in drug discovery and development, this method has the potential to streamline the identification of novel therapeutics.

Benzenesulfonamide decorated dihydropyrimidin(thi)ones: carbonic anhydrase profiling and antiproliferative activity

In the last decades, carbonic anhydrases (CAs) have become the top investigated innovative pharmacological targets and, in particular, isoforms IX and XII have been widely studied due to the evidence of their overexpression in hypoxic tumors. The frantic race to find new anticancer agents places the quick preparation of large libraries of putative bioactive compounds as the basis of a successful drug discovery and development programme. In this context, multi-component and, in general, one-step reactions are becoming very popular and, among them, Biginelli's reaction gave clean and easy-to-isolate products. Thus, we synthesized a series of Biginelli's products (10-17a-b) and similar derivatives (20-21) bearing the benzenesulfonamide moiety, which is known to inhibit CA enzymes. Through the stopped-flow technique, we were able to assess their ability to inhibit the targeted CAs IX and XII in the nanomolar range with promising selectivity over the physiologically relevant isoforms I and II. Crystallography studies and docking simulations helped us to gain insight into the interaction patterns established in the enzyme-inhibitor complex. From a chemical similarity-based screening of in-house libraries of compounds, a diphenylpyrimidine (23) emerged. The surprisingly potent inhibitory activity of 23 for CAs IX and XII along with its strong antiproliferative effect on two (triple-negative breast cancer MDA-MB-231 and glioblastoma U87MG) cell lines laid the foundation for further investigation, again confirming the key role of CAs in cancer.

Illuminating Dark Chemical Matter Using the Cell Painting Assay

Screening for small-molecule modulators of disease-relevant targets and phenotypes is the first step on the way to new drugs. Large compound libraries have been synthesized by academia and, particularly, pharmaceutical companies to meet the need for novel chemical entities that are as diverse as possible. Screening of these compound libraries revealed a portion of small molecules that is inactive in more than 100 different assays and was therefore termed "dark chemical matter" (DCM). Deorphanization of DCM promises to yield very selective compounds as they are expected to have less off-target effects. We employed morphological profiling using the Cell Painting assay to detect bioactive DCM. Within the DCM collection, we identified bioactive compounds and confirmed several modulators of microtubules, DNA synthesis, and pyrimidine biosynthesis. Profiling approaches are, therefore, powerful tools to probe compound collections for bioactivity in an unbiased manner and are particularly suitable for deorphanization of DCM.

In silico identification of a novel Cdc2-like kinase 2 (CLK2) inhibitor in triple negative breast cancer

Dysregulation of RNA splicing processes is intricately linked to tumorigenesis in various cancers, especially breast cancer. Cdc2-like kinase 2 (CLK2), an oncogenic RNA-splicing kinase pivotal in breast cancer, plays a significant role, particularly in the context of triple-negative breast cancer (TNBC), a subtype marked by substantial medical challenges due to its low survival rates. In this study, we employed a structure-based virtual screening (SBVS) method to identify potential CLK2 inhibitors with novel chemical structures for treating TNBC. Compound 670551 emerged as a novel CLK2 inhibitor with a 50% inhibitory concentration (IC50) value of 619.7 nM. Importantly, Compound 670551 exhibited high selectivity for CLK2 over other protein kinases. Functionally, this compound significantly reduced the survival and proliferation of TNBC cells. Results from a cell-based assay demonstrated that this inhibitor led to a decrease in RNA splicing proteins, such as SRSF4 and SRSF6, resulting in cell apoptosis. In summary, we identified a novel CLK2 inhibitor as a promising potential treatment for TNBC therapy.

Identification of small molecule inhibitors of RAD52 for breast cancer therapy: in silico approach

The breast cancer susceptibility gene 1/2 (BRCA1/2) are the key regulators in maintaining the genomic integrity and mutations in these genes have been associated with development of breast and ovarian cancers. Also, synthetic lethality has been shown in BRCA1/2 deficient cancers, when the RAD52 gene is silenced by shRNA or small molecules aptamers, suggesting a role for RAD52 in the breast cancers pathogenesis. Thus, to find the potential inhibitors of RAD52, a collection of 21,000 compounds from the ChemBridge screening library was screened to conduct molecular docking and molecular dynamics simulation (MD) against RAD52. Further, the results were validated by a density functional theory (DFT) analysis and using post-dynamics free energy calculations. Out of all screened molecules, the docking study revealed five compounds were found to have promising activities against RAD52. Moreover, the catalytic amino acid residues of RAD52 developed stable contacts with compound 8758 and 10593, as anticipated by DFT calculation, MD simulation, and post dynamics MM-GBSA energy calculation. It appears that compound 8758 is the best inhibitor against RAD52 followed by 10593 compared to the other top hits, in terms of the HOMO orbital energy (-1.0966 eV and -1.2136 eV) from DFT and the post dynamics binding free energy calculation (-54.71 and -52.43 Kcal/mol). Furthermore, a drug-like properties of lead molecules (8758 and 10593) were also seen via ADMET analysis. Based on our computational analysis, we hypothesize that a small molecule 8758 and 10593 possess the therapeutic potential in the management for breast cancer patients with a BRCA mutation via targeting RAD52.Communicated by Ramaswamy H. Sarma.

Identification of potent histone deacetylase 2 (HDAC2) inhibitors through combined structure and ligand-based designs and molecular modelling approach

Histone deacetylase 2 (HDAC2) is associated with various neuropathic degenerative diseases and is considered a novel target for Alzheimer's disease (AD). Elevated levels of HDAC2 trigger excitatory neurotransmission and reduce synaptic plasticity, synaptic number, and memory formation. In the current study, we identified HDAC2 inhibitors using an integrated structure and ligand-based approaches to drug design. Three pharmacophore models were generated by using different pharmacophoric features and validated using the Enrichment factor (EF), Güner-henry (GH) score, and percentage yield. The model of choice was used to screen a library of Zinc-15 compounds and interfering compounds were eliminated by using drug likeliness and PAINS filtering. Further, docking studies in three stages were carried out to obtain hits with good binding energies and were followed by ADMET studies yielding three virtual hits. The virtual hits, i.e. ZINC000008184553, ZINC0000013641114, and ZINC000032533141, were subjected to molecular dynamics simulation studies. Compound ZINC000008184553, identified as lead, was found to have optimal stability, low toxicity under simulated conditions, and may potentially inhibit HDAC2.Communicated by Ramaswamy H. Sarma.

Creating a more strategic small molecule biophysical hit characterization workflow

To confirm target engagement of hits from our high-throughput screening efforts, we ran biophysical assays on several hundreds of hits from 15 different high-throughput screening campaigns. Analyzing the biophysical assay results from these screening campaigns led us to conclude that we could be more strategic in our biophysical analysis of hits by first confirming activity in a thermal shift assay (TSA) and then confirming activity in either a surface plasmon resonance (SPR) assay or a temperature-related intensity change (TRIC) assay. To understand how this new workflow shapes the quality of the final hits, we compared TSA/SPR or TSA/TRIC confirmed and unconfirmed hits to one another using four measures of compound quality: quantitative estimate of drug-likeness (QED), Pan-Assay Interference Compounds (PAINS), promiscuity, and aqueous solubility. In general, we found that the biophysically confirmed hits performed better in the compound quality metrics than the unconfirmed hits, demonstrating that our workflow not only confirmed target engagement of the hits but also enriched for higher quality hits.

The coronavirus nsp15 endoribonuclease: A puzzling protein and pertinent antiviral drug target

The SARS-CoV-2 pandemic has bolstered unprecedented research efforts to better understand the pathogenesis of coronavirus (CoV) infections and develop effective therapeutics. We here focus on non-structural protein nsp15, a hexameric component of the viral replication-transcription complex (RTC). Nsp15 possesses uridine-specific endoribonuclease (EndoU) activity for which some specific cleavage sites were recently identified in viral RNA. By preventing accumulation of viral dsRNA, EndoU helps the virus to evade RNA sensors of the innate immune response. The immune-evading property of nsp15 was firmly established in several CoV animal models and makes it a pertinent target for antiviral therapy. The search for nsp15 inhibitors typically proceeds via compound screenings and is aided by the rapidly evolving insight in the protein structure of nsp15. In this overview, we broadly cover this fascinating protein, starting with its structure, biochemical properties and functions in CoV immune evasion. Next, we summarize the reported studies in which compound screening or a more rational method was used to identify suitable leads for nsp15 inhibitor development. In this way, we hope to raise awareness on the relevance and druggability of this unique CoV protein.

Carltonine-derived compounds for targeted butyrylcholinesterase inhibition

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

Seclidemstat blocks the transcriptional function of multiple FET-fusion oncoproteins

Genes encoding the RNA-binding proteins FUS, EWSR1, and TAF15 (FET proteins) are involved in chromosomal translocations in rare sarcomas. FET-rearranged sarcomas are often aggressive malignancies affecting patients of all ages. New therapies are needed. These translocations fuse the 5' portion of the FET gene with a 3' partner gene encoding a transcription factor (TF). The resulting fusion proteins are oncogenic TFs with a FET protein low complexity domain (LCD) and a DNA binding domain. FET fusion proteins have proven stubbornly difficult to target directly and promising strategies target critical co-regulators. One candidate is lysine specific demethylase 1 (LSD1). LSD1 is recruited by multiple FET fusions, including EWSR1::FLI1. LSD1 promotes EWSR1::FLI1 activity and treatment with the noncompetitive inhibitor SP-2509 blocks EWSR1::FLI1 transcriptional function. A similar molecule, seclidemstat (SP-2577), is currently in clinical trials for FET-rearranged sarcomas (NCT03600649). However, whether seclidemstat has pharmacological activity against FET fusions has not been demonstrated. Here, we evaluate the in vitro potency of seclidemstat against multiple FET-rearranged sarcoma cell lines, including Ewing sarcoma, desmoplastic small round cell tumor, clear cell sarcoma, and myxoid liposarcoma. We also define the transcriptomic effects of seclidemstat treatment and evaluated the activity of seclidemstat against FET fusion transcriptional regulation. Seclidemstat showed potent activity in cell viability assays across FET-rearranged sarcomas and disrupted the transcriptional function of all tested fusions. Though epigenetic and targeted inhibitors are unlikely to be effective as a single agents in the clinic, these data suggest seclidemstat remains a promising new treatment strategy for patients with FET-rearranged sarcomas.

The small molecule CBR-5884 inhibits the Candida albicans phosphatidylserine synthase

Systemic infections by Candida spp. are associated with high mortality rates, partly due to limitations in current antifungals, highlighting the need for novel drugs and drug targets. The fungal phosphatidylserine synthase, Cho1, from Candida albicans is a logical antifungal drug target due to its importance in virulence, absence in the host, and conservation among fungal pathogens. Inhibitors of Cho1 could serve as lead compounds for drug development, so we developed a target-based screen for inhibitors of purified Cho1. This enzyme condenses serine and cytidyldiphosphate-diacylglycerol (CDP-DAG) into phosphatidylserine (PS) and releases cytidylmonophosphate (CMP). Accordingly, we developed an in vitro nucleotidase-coupled malachite-green-based high throughput assay for purified C. albicans Cho1 that monitors CMP production as a proxy for PS synthesis. Over 7,300 molecules curated from repurposing chemical libraries were interrogated in primary and dose-responsivity assays using this platform. The screen had a promising average Z' score of ~0.8, and seven compounds were identified that inhibit Cho1. Three of these, ebselen, LOC14, and CBR-5884, exhibited antifungal effects against C. albicans cells, with fungicidal inhibition by ebselen and fungistatic inhibition by LOC14 and CBR-5884. Only CBR-5884 showed evidence of disrupting in vivo Cho1 function by inducing phenotypes consistent with the cho1∆∆ mutant, including a reduction of cellular PS levels. Kinetics curves and computational docking indicate that CBR-5884 competes with serine for binding to Cho1 with a Ki of 1,550 ± 245.6 nM. Thus, this compound has the potential for development into an antifungal compound.

IMPORTANCE

Fungal phosphatidylserine synthase (Cho1) is a logical antifungal target due to its crucial role in the virulence and viability of various fungal pathogens, and since it is absent in humans, drugs targeted at Cho1 are less likely to cause toxicity in patients. Using fungal Cho1 as a model, there have been two unsuccessful attempts to discover inhibitors for Cho1 homologs in whole-cell screens prior to this study. The compounds identified in these attempts do not act directly on the protein, resulting in the absence of known Cho1 inhibitors. The significance of our research is that we developed a high-throughput target-based assay and identified the first Cho1 inhibitor, CBR-5884, which acts both on the purified protein and its function in the cell. This molecule acts as a competitive inhibitor with a Ki value of 1,550 ± 245.6 nM and, thus, has the potential for development into a new class of antifungals targeting PS synthase.

Rational Design of Drugs Targeting G-Protein-Coupled Receptors: Ligand Search and Screening

G protein-coupled receptors (GPCRs) are transmembrane proteins that participate in many physiological processes and represent major pharmacological targets. Recent advances in structural biology of GPCRs have enabled the development of drugs based on the receptor structure (structure-based drug design, SBDD). SBDD utilizes information about the receptor-ligand complex to search for suitable compounds, thus expanding the chemical space of possible receptor ligands without the need for experimental screening. The review describes the use of structure-based virtual screening (SBVS) for GPCR ligands and approaches for the functional testing of potential drug compounds, as well as discusses recent advances and successful examples in the application of SBDD for the identification of GPCR ligands.

Weak links: Advancing target-based drug discovery by identifying the most vulnerable targets

Mycobacterium tuberculosis remains the most common infectious killer worldwide despite decades of antitubercular drug development. Effectively controlling the tuberculosis (TB) pandemic will require innovation in drug discovery. In this review, we provide a brief overview of the two main approaches to discovering new TB drugs-phenotypic screens and target-based drug discovery-and outline some of the limitations of each method. We then explore recent advances in genetic tools that aim to overcome some of these limitations. In particular, we highlight a novel metric to prioritize essential targets, termed vulnerability. Stratifying targets based on their vulnerability presents new opportunities for future target-based drug discovery campaigns.

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.

Machine Learning Assisted Hit Prioritization for High Throughput Screening in Drug Discovery

Efficient prioritization of bioactive compounds from high throughput screening campaigns is a fundamental challenge for accelerating drug development efforts. In this study, we present the first data-driven approach to simultaneously detect assay interferents and prioritize true bioactive compounds. By analyzing the learning dynamics during training of a gradient boosting model on noisy high throughput screening data using a novel formulation of sample influence, we are able to distinguish between compounds exhibiting the desired biological response and those producing assay artifacts. Therefore, our method enables false positive and true positive detection without relying on prior screens or assay interference mechanisms, making it applicable to any high throughput screening campaign. We demonstrate that our approach consistently excludes assay interferents with different mechanisms and prioritizes biologically relevant compounds more efficiently than all tested baselines, including a retrospective case study simulating its use in a real drug discovery campaign. Finally, our tool is extremely computationally efficient, requiring less than 30 s per assay on low-resource hardware. As such, our findings show that our method is an ideal addition to existing false positive detection tools and can be used to guide further pharmacological optimization after high throughput screening campaigns.

RDCanon: A Python Package for Canonicalizing the Order of Tokens in SMARTS Queries

SMARTS is a widely used language in cheminformatics for defining substructural queries for database lookups, reaction templates for chemical transformations, and other applications. As an extension to SMILES, many SMARTS patterns can represent the same query. Despite this, no canonicalization algorithm invariant of the line notation sequence or atomic numbering is publicly available. Here, we introduce RDCanon, an open-source Python package that can be used to standardize SMARTS queries. RDCanon is designed to ensure that the sequence of atomic queries remains consistent for all graphs representing the same substructure query and to ensure a canonical sequence of primitives within each individual atom query; furthermore, the algorithm can be applied to canonicalize the order of reactants, agents, and products and their atom map numbers in reaction SMARTS templates. As part of its canonicalization algorithm, RDCanon provides a mechanism in which the canonicalized SMARTS is optimized for speed against specific molecular databases. Several case studies are provided to showcase improved efficiency in substructure matching and retrosynthetic analysis.

Structure-based discovery of small molecule inhibitors of FKBP51-Hsp90 protein-protein interaction

The heat shock protein 90 kDa (Hsp90) molecular chaperone machinery is responsible for the folding and activation of hundreds of important clients such as kinases, steroid hormone receptors, transcription factors, etc. This process is dynamically regulated in an ATP-dependent manner by Hsp90 co-chaperones including a group of tetratricopeptide (TPR) motif proteins that bind to the C-terminus of Hsp90. Among these TPR containing co-chaperones, FK506-binding protein 51 kDa (FKBP51) is reported to play an important role in stress-related pathologies, psychiatric disorders, Alzheimer's disease, and cancer, making FKBP51-Hsp90 interaction a potential therapeutic target. In this study, we report identification of potent and selective inhibitors of FKBP51-Hsp90 protein-protein interaction using a structure-based virtual screening approach. Upon in vitro evaluation, the identified hits show a considerable degree of selectivity towards FKBP51 over other TPR proteins, particularly for highly homologous FKBP52. Tyr355 of FKBP51 emerged as an important contributor to inhibitor's specificity. Additionally, we demonstrate the impact of these inhibitors on cellular energy metabolism, and neurite outgrowth, which are subjects of FKBP51 regulation. Overall, the results from this study highlight a novel pharmacological approach towards regulation of FKBP51 function and more generally, Hsp90 function via its interaction with TPR co-chaperones.

Abiotic Small Molecule Inhibitors and Activators of the LasR Quorum Sensing Receptor in Pseudomonas aeruginosa with Potencies Comparable or Surpassing N-Acyl Homoserine Lactones

The opportunistic pathogen Pseudomonas aeruginosa controls almost 10% of its genome, including myriad virulence genes, via a cell-to-cell chemical communication system called quorum sensing (QS). Small molecules that either inhibit or activate QS in P. aeruginosa represent useful research tools to study the role of this signaling pathway in infection and interrogate its viability as an antivirulence target. However, despite active research in this area over the past 20+ years, there are relatively few synthetic compounds known to strongly inhibit or activate QS in P. aeruginosa. Most reported QS modulators in this pathogen are of low potency or have structural liabilities that limit their application in biologically relevant environments such as mimics of the native N-acyl l-homoserine lactone (AHL) signals. Here, we report the results of a high-throughput screen for abiotic small molecules that target LasR, a key QS regulator in P. aeruginosa. We screened a 25,000-compound library and discovered four new structural classes of abiotic LasR modulators. These compounds include antagonists that surpass the potency of all known AHL-type compounds and mimetics thereof, along with an agonist with potency approaching that of LasR's native ligand. The novel structures of this compound set, along with their anticipated robust physicochemical profiles, underscore their potential value as probe molecules to interrogate the roles of QS in this formidable pathogen.

Identification of potential JNK3 inhibitors through virtual screening, molecular docking and molecular dynamics simulation as therapeutics for Alzheimer's disease

Alzheimer's disease (AD) is a complex neurological disorder and no effective drug is available for its treatment. Numerous pathological conditions are believed to be responsible for the initiation and development of AD including c-Jun N-terminal kinases (JNKs). The JNKs are one of the enzymes from the mitogen-activated protein kinase (MAPK) family that controls the phosphorylation of various transcription factors on serine and threonine residues, and hold significant responsibilities in tasks like gene expression, cell proliferation, differentiation, and apoptosis. Since, JNK3 is primarily expressed in the brain hence its increased levels in the brain are associated with the AD pathology promoting neurofibrillary tangles, senile plaques, neuroinflammation, and nerve cell apoptosis. The current research work is focused on the development of novel JNK inhibitors as therapeutics for AD employing a structure-based virtual screening (SBVS) approach. The ZINC database (14634052 compounds) was investigated after employing pan assay interference (PAINs), drug-likeness, and diversity picking filter to distinguish molecules interacting with JNK3 by following three docking precision criteria: High Throughput Virtual Screening (HTVS), Standard Precision (SP), and Extra Precision (XP) & MMGBSA. Five lead molecules showed a better docking score in the range of -13.091 to -14.051 kcal/mol better than the reference compound (- 11.828 kcal/mol). The lead compounds displayed acceptable pharmacokinetic properties and were subjected to molecular dynamic simulations of 100 ns and binding free energy calculations. All the lead molecules showed stable RMSD and hydrogen bond interactions throughout the trajectory. The ∆GMM/PBSA_total score for the lead compounds ZINC220382956, ZINC147071339, ZINC207081127, ZINC205151456, ZINC1228819126, and CC-930 was calculated and found to be - 31.39, - 42.8, - 37.04, - 39.01, - 36.5, - 34.16 kcal/mol, respectively. Thus, it was concluded that the lead molecules identified in these studies have the potential to be explored as potent JNK3 inhibitors.

Super-resolution imaging for in situ monitoring sub-cellular micro-dynamics of small molecule drug

Small molecule drugs play a pivotal role in the arsenal of anticancer pharmacological agents. Nonetheless, their small size poses a challenge when directly visualizing their localization, distribution, mechanism of action (MOA), and target engagement at the subcellular level in real time. We propose a strategy for developing triple-functioning drug beacons that seamlessly integrate therapeutically relevant bioactivity, precise subcellular localization, and direct visualization capabilities within a single molecular entity. As a proof of concept, we have meticulously designed and constructed a boronic acid fluorescence drug beacon using coumarin-hemicyanine (CHB). Our CHB design includes three pivotal features: a boronic acid moiety that binds both adenosine triphosphate (ATP) and adenosine diphosphate (ADP), thus depleting their levels and disrupting the energy supply within mitochondria; a positively charged component that targets the drug beacon to mitochondria; and a sizeable conjugated luminophore that emits fluorescence, facilitating the application of structured illumination microscopy (SIM). Our study indicates the exceptional responsiveness of our proof-of-concept drug beacon to ADP and ATP, its efficacy in inhibiting tumor growth, and its ability to facilitate the tracking of ADP and ATP distribution around the mitochondrial cristae. Furthermore, our investigation reveals that the micro-dynamics of CHB induce mitochondrial dysfunction by causing damage to the mitochondrial cristae and mitochondrial DNA. Altogether, our findings highlight the potential of SIM in conjunction with visual drug design as a potent tool for monitoring the in situ MOA of small molecule anticancer compounds. This approach represents a crucial advancement in addressing a current challenge within the field of small molecule drug discovery and validation.

Molecular Dynamics of Outer Membrane-Embedded Polysaccharide Secretion Porins Reveals Closed Resting-State Surface Gates Targetable by Virtual Fragment Screening for Drug Hotspot Identification

Recent advances in iterative neural network analyses (e.g., AlphaFold2 and RoseTTA fold) have been revolutionary for protein 3D structure prediction, especially for difficult-to-manipulate α-helical/β-barrel integral membrane proteins. These model structures are calculated based on the coevolution of amino acids within the protein of interest and similarities to existing protein structures; the local effects of the membrane on folding and stability of the calculated model structures are not considered. We recently reported the discovery, 3D modeling, and characterization of 18-β-stranded outer-membrane (OM) WzpX, WzpS, and WzpB β-barrel secretion porins for the exopolysaccharide (EPS), major spore coat polysaccharide (MASC), and biosurfactant polysaccharide (BPS) pathways (respectively) in the Gram-negative social predatory bacterium Myxococcus xanthus DZ2. However, information was not obtained regarding the dynamic behavior of surface-gating WzpX/S/B loop domains or on potential treatments to inactivate these porins. Herein, we developed a molecular dynamics (MD) protocol to study the core stability and loop dynamism of neural network-based integral membrane protein structure models embedded in an asymmetric OM bilayer, using the M. xanthus WzpX, WzpS, and WzpB proteins as test candidates. This was accomplished through integration of the CHARMM-graphical user interface (GUI) and Molecular Operating Environment (MOE) workflows to allow for a rapid simulation system setup and facilitate data analysis. In addition to serving as a method of model structure validation, our molecular dynamics simulations revealed a minimal movement of extracellular WzpX/S/B loops in the absence of an external stimulus as well as druggable cavities between the loops. Virtual screening of a commercial fragment library against these cavities revealed putative fragment-binding hotspots on the cell-surface face of each β-barrel, along with key interacting residues, and identified promising hits for the design of potential binders capable of plugging the β-barrels and inhibiting polysaccharide secretion.

DrugHIVE: Target-specific spatial drug design and optimization with a hierarchical generative model

Rapid advancement in the computational methods of structure-based drug design has led to their widespread adoption as key tools in the early drug development process. Recently, the remarkable growth of available crystal structure data and libraries of commercially available or readily synthesizable molecules have unlocked previously inaccessible regions of chemical space for drug development. Paired with improvements in virtual ligand screening methods, these expanded libraries are having a significant impact on the success of early drug design efforts. However, screening-based methods are limited in their scalability due to computational limits and the sheer scale of drug-like space. An approach within the quickly evolving field of artificial intelligence (AI), deep generative modeling, is extending the reach of molecular design beyond classical methods by learning the fundamental intra- and inter-molecular relationships in drug-target systems from existing data. In this work we introduce DrugHIVE, a deep hierarchical structure-based generative model that enables fine-grained control over molecular generation. Our model outperforms state of the art autoregressive and diffusion-based methods on common benchmarks and in speed of generation. Here, we demonstrate DrugHIVEs capacity to accelerate a wide range of common drug design tasks such as de novo generation, molecular optimization, scaffold hopping, linker design, and high throughput pattern replacement. Our method is highly scalable and can be applied to high confidence AlphaFold predicted receptors, extending our ability to generate high quality drug-like molecules to a majority of the unsolved human proteome.

Development of Selective Pyrido[2,3-d]pyrimidin-7(8H)-one-Based Mammalian STE20-Like (MST3/4) Kinase Inhibitors

Mammalian STE20-like (MST) kinases 1-4 play key roles in regulating the Hippo and autophagy pathways, and their dysregulation has been implicated in cancer development. In contrast to the well-studied MST1/2, the roles of MST3/4 are less clear, in part due to the lack of potent and selective inhibitors. Here, we re-evaluated literature compounds, and used structure-guided design to optimize the p21-activated kinase (PAK) inhibitor G-5555 (8) to selectively target MST3/4. These efforts resulted in the development of MR24 (24) and MR30 (27) with good kinome-wide selectivity and high cellular potency. The distinct cellular functions of closely related MST kinases can now be elucidated with subfamily-selective chemical tool compounds using a combination of the MST1/2 inhibitor PF-06447475 (2) and the two MST3/4 inhibitors developed. We found that MST3/4-selective inhibition caused a cell-cycle arrest in the G1 phase, whereas MST1/2 inhibition resulted in accumulation of cells in the G2/M phase.

Navigating the Chemical Space of ENR Inhibitors: A Comprehensive Analysis

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

Identifying ligands for the PHD1 finger of KDM5A through high-throughput screening

PHD fingers are a type of chromatin reader that primarily recognize chromatin as a function of lysine methylation state. Dysregulated PHD fingers are implicated in various human diseases, including acute myeloid leukemia. Targeting PHD fingers with small molecules is considered challenging as their histone tail binding pockets are often shallow and surface-exposed. The KDM5A PHD1 finger regulates the catalytic activity of KDM5A, an epigenetic enzyme often misregulated in cancers. To identify ligands that disrupt the PHD1-histone peptide interaction, we conducted a high-throughput screen and validated hits by orthogonal methods. We further elucidated structure-activity relationships in two classes of compounds to identify features important for binding. Our investigation offers a starting point for further optimization of small molecule PHD1 ligands.

An integrated docking and molecular dynamics simulation approach to discover potential inhibitors of activin receptor-like kinase 1

Activin receptor-like kinase 1 (ALK1) is a transmembrane receptor involved in crucial signaling pathways associated with angiogenesis and vascular development. Inhibition of ALK1 signaling has emerged as a promising therapeutic strategy for various angiogenesis-related diseases, including cancer and hereditary hemorrhagic telangiectasia. This study aimed to investigate the potential of phytoconstituents as inhibitors of ALK1 using a combined approach of virtual screening and molecular dynamics (MDs) simulations. Phytoconstituents from the IMPPAT 2.0 database underwent virtual screening to identify potential inhibitors of ALK1. The compounds were initially filtered based on physicochemical parameters, following Lipinski's rules and the PAINS filter. Subsequently, compounds demonstrating high binding affinities in docking analysis were further analyzed. Additional assessments, including ADMET, PAINS, and PASS evaluations, were conducted to identify more potent hits. Through interaction analysis, a phytoconstituent, Candidine, exhibited appreciable affinity and specific interactions with the ALK1 active site. To validate the results, MD simulations and principal components analysis were performed. The MD simulations demonstrated that Candidine stabilized the ALK1 structure and reduced conformational fluctuations. In conclusion, Candidine shows promising potential as binding partners of ALK1. These findings provide a foundation for further exploration and development of Candidine as a lead molecule for therapeutic interventions targeting ALK1-associated diseases.

Rational design and virtual screening identify mimetics of the RXR agonist valerenic acid

The ligand-sensing transcription factor retinoid X receptor (RXR) is the universal heterodimer partner of nuclear receptors and involved in multiple physiological processes. Its pharmacological modulation holds therapeutic potential in cancer and neurodegeneration but many available RXR ligands lack specificity. The sesquiterpenoid valerenic acid has been identified as RXR agonist with unprecedented subtype and homodimer preference. Here, we identified simplified mimetics of the complex natural product by rational design and virtual screening that exhibited similar activity profiles on RXR and informed about structural elements contributing to the favorable activity.

Chemical Multiverse and Diversity of Food Chemicals

Food chemicals have a fundamental role in our lives, with an extended impact on nutrition, disease prevention, and marked economic implications in the food industry. The number of food chemical compounds in public databases has substantially increased in the past few years, which can be characterized using chemoinformatics approaches. We and other groups explored public food chemical libraries containing up to 26,500 compounds. This study aimed to analyze the chemical contents, diversity, and coverage in the chemical space of food chemicals and additives and, from here on, food components. The approach to food components addressed in this study is a public database with more than 70,000 compounds, including those predicted via omics techniques. It was concluded that food components have distinctive physicochemical properties and constitutional descriptors despite sharing many chemical structures with natural products. Food components, on average, have large molecular weights and several apolar structures with saturated hydrocarbons. Compared to reference databases, food component structures have low scaffold and fingerprint-based diversity and high structural complexity, as measured by the fraction of sp3 carbons. These structural features are associated with a large fraction of macronutrients as lipids. Lipids in food components were decompiled by an analysis of the maximum common substructures. The chemical multiverse representation of food chemicals showed a larger coverage of chemical space than natural products and FDA-approved drugs by using different sets of representations.

Azetidines Kill Multidrug-Resistant Mycobacterium tuberculosis without Detectable Resistance by Blocking Mycolate Assembly

Tuberculosis (TB) is the leading cause of global morbidity and mortality resulting from infectious disease, with over 10.6 million new cases and 1.4 million deaths in 2021. This global emergency is exacerbated by the emergence of multidrug-resistant MDR-TB and extensively drug-resistant XDR-TB; therefore, new drugs and new drug targets are urgently required. From a whole cell phenotypic screen, a series of azetidines derivatives termed BGAz, which elicit potent bactericidal activity with MIC99 values <10 μM against drug-sensitive Mycobacterium tuberculosis and MDR-TB, were identified. These compounds demonstrate no detectable drug resistance. The mode of action and target deconvolution studies suggest that these compounds inhibit mycobacterial growth by interfering with cell envelope biogenesis, specifically late-stage mycolic acid biosynthesis. Transcriptomic analysis demonstrates that the BGAz compounds tested display a mode of action distinct from the existing mycobacterial cell wall inhibitors. In addition, the compounds tested exhibit toxicological and PK/PD profiles that pave the way for their development as antitubercular chemotherapies.

DMSO-Related Effects on Ligand-Binding Properties of Lysine Methyltransferases G9a and SETD8

Being the standard solvent for preparing stock solutions of compounds for drug discovery, DMSO is always present in assay buffers in concentrations ranging from 0.1 % to 5 % (v/v). Even at the lowest concentrations, DMSO-containing solutions can have significant effects on individual proteins and possible pitfalls cannot be eliminated. Herein, we used two protein systems, the lysine methyltransferases G9a/KMT1 C and SETD8/KMT5 A, to study the effects of DMSO on protein stability and on the binding of the corresponding inhibitors, using different biophysical methods such as nano Differential Scanning Fluorimetry (nanoDSF), Differential Scanning Fluorimetry (DSF), microscale thermophoresis (MST), and surface plasmon resonance (SPR), all widely used in drug discovery screening campaigns. We demonstrated that the effects of DMSO are protein- and technique-dependent and cannot be predicted or extrapolated on the basis of previous studies using different proteins and/or different assays. Moreover, we showed that the application of orthogonal biophysical methods can lead to different binding affinity data, thus confirming the importance of using at least two different orthogonal assays in screening campaigns. This variability should be taken into account in the selection and characterization of hit compounds, in order to avoid data misinterpretation.

Camptothecin structure simplification elaborated new imidazo[2,1-b]quinazoline derivative as a human topoisomerase I inhibitor with efficacy against bone cancer cells and colon adenocarcinoma

Camptothecin is a pentacyclic natural alkaloid that inhibits the hTop1 enzyme involved in DNA transcription and cancer cell growth. Camptothecin structure pitfalls prompted us to design new congeners using a structure simplification strategy to reduce the ring extension number from pentacyclic to tetracyclic while maintaining potential stacking of the new compounds with the DNA base pairs at the Top1-mediated cleavage complex and aqueous solubility, as well as minimizing compound-liver toxicity. The principal axis of this study was the verification of hTop1 inhibiting activity as a possible mechanism of action and the elaboration of new simplified inhibitors with improved pharmacodynamic and pharmacokinetic profiling using three structure panels (A-C) of (isoquinolinoimidazoquinazoline), (imidazoquinazoline), and (imidazoisoquinoline), respectively. DNA relaxation assay identified five compounds as hTop1 inhibitors belonging to the imidazoisoquinolines 3a,b, the imidazoquinazolines 12, and the isoquinolinoimidazoquinazolines 7a,b. In an MTT cytotoxicity assay against different cancer cell lines, compound 12 was the most potent against HOS bone cancer cells (IC50 = 1.47 μM). At the same time, the other inhibitors had no detectable activity against any cancer cell type. Compound (12) demonstrated great penetrating power in the HOS cancer cells' 3D-multicellular tumor spheroid model. Bioinformatics research of the hTop1 gene revealed that the TP53 cell proliferative gene is in the network of hTop1. The finding is confirmed empirically using the gene expression assay that proved the increase in p53 expression. The impact of structure simplification on compound 12 profile, characterized by the absence of acute oral liver toxicity when compared to Doxorubicin as a standard inhibitor, the lethal dose measured on Swiss Albino female mice and reported at LD50 = 250 mg/kg, and therapeutic significance in reducing colon adenocarcinoma tumor volume by 75.36 % after five weeks of treatment with compound 12. The molecular docking solutions of the active CPT-based derivative 12 and the inactive congener 14 into the active site of hTop1 and the activity cliffing of such MMP directed us to recommend the addition of HBD and HBA variables to compound 12 imidazoquinazoline core scaffold to enhance the potency via hydrogen bond formation with the major groove amino acids (Asp533, Lys532) as well as maintaining the hydrogen bond with the minor groove amino acid Arg364.

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

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

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.

Synthesis, Characterizations, and Quantum Chemical Investigations on Imidazo[1,2-a]pyrimidine-Schiff Base Derivative: (E)-2-Phenyl-N-(thiophen-2-ylmethylene)imidazo[1,2-a]pyrimidin-3-amine

In this study, (E)-2-phenyl-N-(thiophen-2-ylmethylene)imidazo[1,2-a]pyrimidin-3-amine (3) is synthesized, and detailed spectral characterizations using 1H NMR, 13C NMR, mass, and Fourier transform infrared (FT-IR) spectroscopy were performed. The optimized geometry was computed using the density functional theory method at the B3LYP/6-311++G(d,p) basis set. The theoretical FT-IR and NMR (1H and 13C) analysis are agreed to validate the structural assignment made for (3). Frontier molecular orbitals, molecular electrostatic potential, Mulliken atomic charge, electron localization function, localized orbital locator, natural bond orbital, nonlinear optical, Fukui functions, and quantum theory of atoms in molecules analyses are undertaken and meticulously interpreted, providing profound insights into the molecular nature and behaviors. In addition, ADMET and drug-likeness studies were carried out and investigated. Furthermore, molecular docking and molecular dynamics simulations have been studied, indicating that this is an ideal molecule to develop as a potential vascular endothelial growth factor receptor-2 inhibitor.

A cell-permeable probe for the labelling of a bacterial glycosyltransferase and virulence factor

Chemical probes for bacterial glycosyltransferases are of interest for applications such as tracking of expression levels, and strain profiling and identification. Existing probes for glycosyltransferases are typically based on sugar-nucleotides, whose charged nature limits their applicability in intact cells. We report the development of an uncharged covalent probe for the bacterial galactosyltransferase LgtC, and its application for the fluorescent labelling of this enzyme in recombinant form, cell lysates, and intact cells. The probe was designed by equipping a previously reported covalent LgtC inhibitor based on a pyrazol-3-one scaffold with a 7-hydroxycoumarin fluorophore. We show that this pyrazol-3-ones scaffold is surprisingly stable in aqueous media, which may have wider implications for the use of pyrazol-3-ones as chemical probes. We also show that the 7-hydroxycoumarin fluorophore leads to an unexpected improvement in activity, which could be exploited for the development of second generation analogues. These results will provide a basis for the development of LgtC-specific probes for the detection of LgtC-expressing bacterial strains.

High-throughput screening of compounds targeting RNA editing in Trypanosoma brucei: Novel molecular scaffolds with broad trypanocidal effects

Mitochondrial uridine insertion/deletion RNA editing, catalyzed by a multiprotein complex (editosome), is essential for gene expression in trypanosomes and Leishmania parasites. As this process is absent in the human host, a drug targeting this mechanism promises high selectivity and reduced toxicity. Here, we successfully miniaturized our FRET-based full-round RNA editing assay, which replicates the complete RNA editing process, adapting it into a 1536-well format. Leveraging this assay, we screened over 100,000 compounds against purified editosomes derived from Trypanosoma brucei, identifying seven confirmed primary hits. We sourced and evaluated various analogs to enhance the inhibitory and parasiticidal effects of these primary hits. In combination with secondary assays, our compounds marked inhibition of essential catalytic activities, including the RNA editing ligase and interactions of editosome proteins. Although the primary hits did not exhibit any growth inhibitory effect on parasites, we describe eight analog compounds capable of effectively killing T. brucei and/or Leishmania donovani parasites within a low micromolar concentration. Whether parasite killing is - at least in part - due to inhibition of RNA editing in vivo remains to be assessed. Our findings introduce novel molecular scaffolds with the potential for broad antitrypanosomal effects.

Design and Diversity Analysis of Chemical Libraries in Drug Discovery

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

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

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

Antiproliferative Activities and SwissADME Predictions of Physicochemical Properties of Carbonyl Group-Modified Rotenone Analogues

Rotenone is a naturally occurring compound shown to exhibit antiproliferative activity against various cancer cell lines, indicating its potential as a lead anticancer agent. However, its toxicity against normal cells has prompted further investigation and chemical modifications. In this study, a library of carbonyl group-modified rotenone derivatives was synthesized and evaluated for their antiproliferative activities against MCF-7 breast cancer cells, A549 human lung carcinoma cells, and HCT116 human colorectal cancer cells using 3-(4, 5-dimethylthiazolyl-2)-2, 5-diphenyltetrazolium bromide (MTT) assay. The results showed several promising compounds that inhibited cell proliferation. Specifically, the oxime and alcohol rotenone derivatives exhibited antiproliferative activities against all 3 cancer cell lines, while the ethoxy, carbamate, and alkene derivatives are selective against MCF-7 (IC50 =5.72 μM), HCT116 (IC50 =8.86 μM), and A549 (IC50 =0.11 μM), respectively. SwissADME analysis showed that the physicochemical properties and drug-likeness of the synthesized rotenone derivatives were within the set limits, suggesting the favorable characteristics of these compounds for drug development. The findings obtained in this work highlight the potential of rotenone derivatives as promising chemotherapeutic candidates.

Structural Fusion of Natural and Synthetic Ligand Features Boosts RXR Agonist Potency

The retinoid X receptors (RXRs) are ligand-activated transcription factors involved in, for example, differentiation and apoptosis regulation. Currently used reference RXR agonists suffer from insufficient specificity and poor physicochemical properties, and improved tools are needed to capture the unexplored therapeutic potential of RXR. Endogenous vitamin A-derived RXR ligands and the natural product RXR agonist valerenic acid comprise acrylic acid residues with varying substitution patterns to engage the critical ionic contact with the binding site arginine. To mimic and exploit this natural ligand motif, we probed its structural fusion with synthetic RXR modulator scaffolds, which had profound effects on agonist activity and remarkably boosted potency of an oxaprozin-derived RXR agonist chemotype. Bioisosteric replacement of the acrylic acid to overcome its pan-assay interference compounds (PAINS) character enabled the development of a highly optimized RXR agonist chemical probe.

Synthesis and Pro-Apoptotic Effects of Nitrovinylanthracenes and Related Compounds in Chronic Lymphocytic Leukaemia (CLL) and Burkitt's Lymphoma (BL)

Chronic lymphocytic leukaemia (CLL) is a malignancy of the immune B lymphocyte cells and is the most common leukaemia diagnosed in developed countries. In this paper, we report the synthesis and antiproliferative effects of a series of (E)-9-(2-nitrovinyl)anthracenes and related nitrostyrene compounds in CLL cell lines and also in Burkitt's lymphoma (BL) cell lines, a rare form of non-Hodgkin's immune B-cell lymphoma. The nitrostyrene scaffold was identified as a lead structure for the development of effective compounds targeting BL and CLL. The series of structurally diverse nitrostyrenes was synthesised via Henry-Knoevenagel condensation reactions. Single-crystal X-ray analysis confirmed the structure of (E)-9-chloro-10-(2-nitrobut-1-en-1-yl)anthracene (19f) and the related 4-(anthracen-9-yl)-1H-1,2,3-triazole (30a). The (E)-9-(2-nitrovinyl)anthracenes 19a, 19g and 19i-19m were found to elicit potent antiproliferative effects in both BL cell lines EBV-MUTU-1 (chemosensitive) and EBV+ DG-75 (chemoresistant) with >90% inhibition at 10 μM. Selected (E)-9-(2-nitrovinyl)anthracenes demonstrated potent antiproliferative activity in CLL cell lines, with IC50 values of 0.17 μM (HG-3) and 1.3 μM (PGA-1) for compound 19g. The pro-apoptotic effects of the most potent compounds 19a, 19g, 19i, 19l and 19m were demonstrated in both CLL cell lines HG-3 and PGA-1. The (E)-nitrostyrene and (E)-9-(2-nitrovinyl)anthracene series of compounds offer potential for further development as novel chemotherapeutics for CLL.

Synthesis of 9-Cinnamyl-9H-purine Derivatives as Novel TLR4/MyD88/NF-κB Pathway Inhibitors for Anti-inflammatory Effects

The novel 9-cinnamyl-9H-purine skeleton, inspired by resveratrol and curcumin, was developed to avoid a pan-assay interference compound (PAINS) related to invalid metabolic pancreas activity (IMPS). It replaced the phenol group with purine analogues, the building blocks of DNA and RNA. Alterations to the hydroxyl group in the cinnamyl group, such as H, Me, or F substitutions, were made to impede its oxidation to a PAINS-associated quinone. Among the compounds tested, 5e significantly inhibited nitric oxide production in LPS-induced macrophages (IC50: 6.4 vs 26.4 μM for resveratrol). 5e also reduced pro-inflammatory cytokine levels (IL-6, TNF-α, IL-1β) and lowered iNOS and COX-2 protein levels. Mechanistically, 5e disrupted the TLR4-MyD88 protein interaction, leading to the suppression of the NF-κB signaling pathway suppression. In an atopic dermatitis mouse model, 5e reduced ear edema and inflammation. These findings indicate that the novel 9-cinnamyl-9H-purine skeleton provides therapeutic insight into treating various human diseases by regulating inflammation.

Kawsar SM. Marine-Derived Compounds as Potential Inhibitors of Hsp90 for Anticancer and Antimicrobial Drug Development: A Comprehensive In Silico Study

Marine compounds constitute a diverse and invaluable resource for the discovery of bioactive substances with promising applications in the pharmaceutical development of anti-inflammatory and antibacterial agents. In this study, a comprehensive methodology was employed, encompassing pharmacophore modeling, virtual screening, in silico ADMET assessment (encompassing aspects of absorption, distribution, metabolism, excretion, and toxicity), and molecular dynamics simulations. These methods were applied to identify new inhibitors targeting the Hsp90 protein (heat shock protein 90), commencing with a diverse assembly of compounds sourced from marine origins. During the virtual screening phase, an extensive exploration was conducted on a dataset comprising 31,488 compounds sourced from the CMNPD database, characterized by a wide array of molecular structures. The principal objective was the development of structure-based pharmacophore models, a valuable approach when the pool of known ligands is limited. The pharmacophore model DDRRR was successfully constructed within the active sites of the Hsp90 crystal structure. Subsequent docking studies led to the identification of six compounds (CMNPD 22591, 9335, 10015, 360799, 15115, and 20988) demonstrating substantial binding affinities, each with values below -8.3 kcal/mol. In the realm of in silico ADMET predictions, five of these compounds exhibited favorable pharmacokinetic properties. Furthermore, molecular dynamics simulations and total binding energy calculations using MM-PBSA indicated that these marine-derived compounds formed exceptionally stable complexes with the Hsp90 receptor over a 100-nanosecond simulation period. These findings underscore the considerable potential of these novel marine compounds as promising candidates for anticancer and antimicrobial drug development.

Small Molecules N-Phenylbenzofuran-2-carboxamide and N-Phenylbenzo[b]thiophene-2-carboxamide Promote Beta-Amyloid (Aβ42) Aggregation and Mitigate Neurotoxicity

This study reports the unusual ability of small molecules N-phenylbenzofuran-2-carboxamide (7a) and N-phenylbenzo[b]thiophene-2-carboxamide (7b) to promote and accelerate Aβ42 aggregation. In the in vitro aggregation kinetic assays, 7a was able to demonstrate rapid increases in Aβ42 fibrillogenesis ranging from 1.5- to 4.7-fold when tested at 1, 5, 10, and 25 μM compared to Aβ42-alone control. Similarly, compound 7b also exhibited 2.9- to 4.3-fold increases in Aβ42 fibrillogenesis at the concentration range tested. Electron microscopy studies at 1, 5, 10, and 25 μM also demonstrate the ability of compounds 7a and 7b to promote and accelerate Aβ42 aggregation with the formation of long, elongated fibril structures. Both 7a and 7b were not toxic to HT22 hippocampal neuronal cells and strikingly were able to prevent Aβ42-induced cytotoxicity in HT22 hippocampal neuronal cells (cell viability ∼74%) compared to the Aβ42-treated group (cell viability ∼20%). Fluorescence imaging studies using BioTracker 490 green, Hoeschst 33342, and the amyloid binding dye ProteoStat further demonstrate the ability of 7a and 7b to promote Aβ42 fibrillogenesis and prevent Aβ42-induced cytotoxicity to HT22 hippocampal neuronal cells. Computational modeling studies suggest that both 7a and 7b can interact with the Aβ42 oligomer and pentamers and have the potential to modulate the self-assembly pathways. The 8-anilino-1-naphthalenesulfonic acid (ANS) dye binding assay also demonstrates the ability of 7a and 7b to expose the hydrophobic surface of Aβ42 to the solvent surface that promotes self-assembly and rapid fibrillogenesis. These studies demonstrate the unique ability of small molecules 7a and 7b to alter the self-assembly and misfolding pathways of Aβ42 by promoting the formation of nontoxic aggregates. These findings have direct implications in the discovery and development of novel small-molecule-based chemical and pharmacological tools to study the Aβ42 aggregation mechanisms, and in the design of novel antiamyloid therapies to treat Alzheimer's disease.

HTS discovery of PARP1-HPF1 complex inhibitors in cancer

PARP1/2 inhibitors (PARPi) are effective clinically used drugs for the treatment of cancers with BRCA deficiencies. PARPi have had limited success and applicability beyond BRCA deficient cancers, and their effect is diminished by resistance mechanisms. The recent discovery of Histone PARylation Factor (HPF1) and the role it plays in the PARylation reaction by forming a shared active site with PARP1 raises the possibility that novel inhibitors that target the PARP1-HPF1 complex can be identified. Herein we describe a simple and cost-effective high-throughput screening (HTS) method aimed at discovering inhibitors of the PARP1-HPF1 complex. Upon HTS validation, we first applied this method to screen a small PARP-focused library of compounds and then scale up our approach using robotic automation to conduct a pilot screen of 10,000 compounds and validating >100 hits. This work demonstrates for the first time the capacity to discover potent inhibitors of the PARP1-HPF1 complex, which may have utility as probes to better understand the DNA damage response and as therapeutics for cancer.

Synthesis, characterization, and biological properties of novel Schiff bases containing pentafluorophenyl hydrazine

In the present study, new Schiff bases derived from pentafluorophenyl-hydrazine (L1, L2, L3, L4) were synthesized and their structures were characterized by 1 H nuclear magnetic resonance spectroscopy (NMR), 13 C NMR, 19 F NMR, fourier transform infrared spectroscopy, and elemental analysis methods. Then, the anticancer activities of the obtained compounds were investigated using three human cancer cell lines (A2780, over; Caco-2, colon; and HT-29 colon carcinoma cell lines). According to the obtained cytotoxicity results, compound number L4 was found to have the highest anticancer activity in A2780 (over) and Caco-2 (colon) cell lines. Furthermore, in silico, ADMET properties, where studies play an important role in the development and prediction of drug compounds, were calculated using web-based platforms. In addition, molecular docking studies were performed to evaluate the binding interactions between the synthesized pentafluorophenyl-hydrazone compounds and the MDM2 protein (4JSC). Both in vitro and in silico results showed that the synthesized compounds could act as potent anticancer agents.

A Multitarget Approach against Neuroinflammation: Alkyl Substituted Coumarins as Inhibitors of Enzymes Involved in Neurodegeneration

Neurodegenerative disorders (NDs) include a large range of diseases characterized by neural dysfunction with a multifactorial etiology. The most common NDs are Alzheimer's disease and Parkinson's disease, in which cholinergic and dopaminergic systems are impaired, respectively. Despite different brain regions being affected, oxidative stress and inflammation were found to be common triggers in the pathogenesis and progression of both diseases. By taking advantage of a multi-target approach, in this work we explored alkyl substituted coumarins as neuroprotective agents, capable to reduce oxidative stress and inflammation by inhibiting enzymes involved in neurodegeneration, among which are Carbonic Anhydrases (CAs), Monoamine Oxidases (MAOs), and Cholinesterases (ChEs). The compounds were synthesized and profiled against the three targeted enzymes. The binding mode of the most promising compounds (7 and 9) within MAO-A and -B was analyzed through molecular modeling studies, providing and explanation for the different selectivities observed for the MAO isoforms. In vitro biological studies using LPS-stimulated rat astrocytes showed that some compounds were able to counteract the oxidative stress-induced neuroinflammation and hamper interleukin-6 secretion, confirming the success of this multitarget approach.

Reversible and irreversible inhibitors of coronavirus Nsp15 endoribonuclease

The emergence of severe acute respiratory syndrome coronavirus 2, the causative agent of coronavirus disease 2019, has resulted in the largest pandemic in recent history. Current therapeutic strategies to mitigate this disease have focused on the development of vaccines and on drugs that inhibit the viral 3CL protease or RNA-dependent RNA polymerase enzymes. A less-explored and potentially complementary drug target is Nsp15, a uracil-specific RNA endonuclease that shields coronaviruses and other nidoviruses from mammalian innate immune defenses. Here, we perform a high-throughput screen of over 100,000 small molecules to identify Nsp15 inhibitors. We characterize the potency, mechanism, selectivity, and predicted binding mode of five lead compounds. We show that one of these, IPA-3, is an irreversible inhibitor that might act via covalent modification of Cys residues within Nsp15. Moreover, we demonstrate that three of these inhibitors (hexachlorophene, IPA-3, and CID5675221) block severe acute respiratory syndrome coronavirus 2 replication in cells at subtoxic doses. This study provides a pipeline for the identification of Nsp15 inhibitors and pinpoints lead compounds for further development against coronavirus disease 2019 and related coronavirus infections.

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

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

Identification of Novel β-Tubulin Inhibitors Using a Combined In Silico/In Vitro Approach

Due to their potential as leads for various therapeutic applications, including as antimitotic and antiparasitic agents, the development of tubulin inhibitors offers promise for drug discovery. In this study, an in silico pharmacophore-based virtual screening approach targeting the colchicine binding site of β-tubulin was employed. Several structure- and ligand-based models for known tubulin inhibitors were generated. Compound databases were virtually screened against the models, and prioritized hits from the SPECS compound library were tested in an in vitro tubulin polymerization inhibition assay for their experimental validation. Out of the 41 SPECS compounds tested, 11 were active tubulin polymerization inhibitors, leading to a prospective true positive hit rate of 26.8%. Two novel inhibitors displayed IC50 values in the range of colchicine. The most potent of which was a novel acetamide-bridged benzodiazepine/benzimidazole derivative with an IC50 = 2.9 μM. The screening workflow led to the identification of diverse inhibitors active at the tubulin colchicine binding site. Thus, the pharmacophore models show promise as valuable tools for the discovery of compounds and as potential leads for the development of cancer therapeutic agents.