Identification and characterization of small molecule inhibitors of the LINE-1 retrotransposon endonuclease.

The long interspersed nuclear element-1 (LINE-1 or L1) retrotransposon is the only active autonomously replicating retrotransposon in the human genome. L1 harms the cell by inserting new copies, generating DNA damage, and triggering inflammation. Therefore, L1 inhibition could be used to treat many diseases associated with these processes. Previous research has focused on inhibition of the L1 reverse transcriptase due to the prevalence of well-characterized inhibitors of related viral enzymes. Here we present the L1 endonuclease as another target for reducing L1 activity. We characterize structurally diverse small molecule endonuclease inhibitors using computational, biochemical, and biophysical methods. We also show that these inhibitors reduce L1 retrotransposition, L1-induced DNA damage, and inflammation reinforced by L1 in senescent cells. These inhibitors could be used for further pharmacological development and as tools to better understand the life cycle of this element and its impact on disease processes.

Reversible male contraception by targeted inhibition of serine/threonine kinase 33.

Men or mice with homozygous serine/threonine kinase 33 (STK33) mutations are sterile owing to defective sperm morphology and motility. To chemically evaluate STK33 for male contraception with STK33-specific inhibitors, we screened our multibillion-compound collection of DNA-encoded chemical libraries, uncovered potent STK33-specific inhibitors, determined the STK33 kinase domain structure bound with a truncated hit CDD-2211, and generated an optimized hit CDD-2807 that demonstrates nanomolar cellular potency (half-maximal inhibitory concentration = 9.2 nanomolar) and favorable metabolic stability. In mice, CDD-2807 exhibited no toxicity, efficiently crossed the blood-testis barrier, did not accumulate in brain, and induced a reversible contraceptive effect that phenocopied genetic STK33 perturbations without altering testis size. Thus, STK33 is a chemically validated, nonhormonal contraceptive target, and CDD-2807 is an effective tool compound.

Virtual Screening of Small Molecules Targeting BCL2 with Machine Learning, Molecular Docking, and MD Simulation.

This study aimed to identify potential BCL-2 small molecule inhibitors using deep neural networks (DNN) and random forest (RF), algorithms as well as molecular docking and molecular dynamics (MD) simulations to screen a library of small molecules. The RF model classified 61% (2355/3867) of molecules as 'Active'. Further analysis through molecular docking with Vina identified CHEMBL3940231, CHEMBL3938023, and CHEMBL3947358 as top-scored small molecules with docking scores of -11, -10.9, and 10.8 kcal/mol, respectively. MD simulations validated these compounds' stability and binding affinity to the BCL2 protein.

An emerging target for male contraception.

An inhibitor of a nonhormonal target is identified using a DNA-encoded chemical library.

Discovery of Novel Small-Molecule-Based Potential PD-L1/EGFR Dual Inhibitors with High Druggability for Glioblastoma Immunotherapy.

Based on the close relationship between programmed death protein ligand 1 (PD-L1) and epidermal growth factor receptor (EGFR) in glioblastoma (GBM), we designed and synthesized a series of small molecules as potential dual inhibitors of EGFR and PD-L1. Among them, compound EP26 exhibited the highest inhibitory activity against EGFR (IC50 = 37.5 nM) and PD-1/PD-L1 interaction (IC50 = 1.77 µM). In addition, EP26 displayed superior in vitro antiproliferative activities and in vitro immunomodulatory effects by promoting U87MG cell death in a U87MG/Jurkat cell coculture model. Furthermore, EP26 possessed favorable pharmacokinetic properties (F = 22%) and inhibited tumor growth (TGI = 92.0%) in a GBM mouse model more effectively than Gefitinib (77.2%) and NP19 (82.8%). Moreover, EP26 increased CD4+ cells and CD8+ cells in tumor microenvironment. Collectively, these results suggest that EP26 represents the first small-molecule-based PD-L1/EGFR dual inhibitor deserving further investigation as an immunomodulating agent for cancer treatment.

Lead compound profiling for small molecule inhibitors of the REV1-CT/RIR Translesion synthesis Protein-Protein interaction.

Translesion synthesis (TLS) is a cellular mechanism through which actively replicating cells recruit specialized, low-fidelity DNA polymerases to damaged DNA to allow for replication past these lesions. REV1 is one of these TLS DNA polymerases that functions primarily as a scaffolding protein to organize the TLS heteroprotein complex and ensure replication occurs in the presence of DNA lesions. The C-Terminal domain of REV1 (REV1-CT) forms many protein-protein interactions (PPIs) with other TLS polymerases, making it essential for TLS function and a promising drug target for anti-cancer drug development. We utilized several lead identification strategies to identify various small molecules capable of disrupting the PPI between REV1-CT and the REV1 Interacting Regions (RIR) present in several other TLS polymerases. These lead compounds were profiled in several in vitro potency and PK assays to identify two scaffolds (1 and 6) as the most promising for further development. Both 1 and 6 synergized with cisplatin in a REV1-dependent fashion and demonstrated promising in vivo PK and toxicity profiles.

Selenization of Small Molecule Drugs: A New Player on the Board.

There is an urgent need to develop safer and more effective modalities for the treatment of a wide range of pathologies due to the increasing rates of drug resistance, undesired side effects, poor clinical outcomes, etc. Throughout the years, selenium (Se) has attracted a great deal of attention due to its important role in human health. Besides, a growing body of work has unveiled that the inclusion of Se motifs into a great number of molecules is a promising strategy for obtaining novel therapeutic agents. In the current Perspective, we have gathered the most recent literature related to the incorporation of different Se moieties into the scaffolds of a wide range of known drugs and their feasible pharmaceutical applications. In addition, we highlight different representative examples as well as provide our perspective on Se drugs and the possible future directions, promises, opportunities, and challenges of this ground-breaking area of research.

Hybrid Small-Molecule/Protein Fluorescent Probes.

Hybrid small-molecule/protein fluorescent probes are powerful tools for visualizing protein localization and function in living cells. These hybrid probes are constructed by diverse site-specific chemical protein labeling approaches through chemical reactions to exogenous peptide/small protein tags, enzymatic post-translational modifications, bioorthogonal reactions for genetically incorporated unnatural amino acids, and ligand-directed chemical reactions. The hybrid small-molecule/protein fluorescent probes are employed for imaging protein trafficking, conformational changes, and bioanalytes surrounding proteins. In addition, fluorescent hybrid probes facilitate visualization of protein dynamics at the single-molecule level and the defined structure with super-resolution imaging. In this review, we discuss development and the bioimaging applications of fluorescent probes based on small-molecule/protein hybrids.

Ligandability Assessment of IL-1ß by Integrated Hit Identification Approaches.

Human interleukin-1ß (IL-1ß) is a pro-inflammatory cytokine that plays a critical role in the regulation of the immune response and the development of various inflammatory diseases. In this publication, we disclose our efforts toward the discovery of IL-1ß binders that interfere with IL-1ß signaling. To this end, several technologies were used in parallel, including fragment-based screening (FBS), DNA-encoded library (DEL) technology, peptide discovery platform (PDP), and virtual screening. The utilization of distinct technologies resulted in the identification of new chemical entities exploiting three different sites on IL-1ß, all of them also inhibiting the interaction with the IL-1R1 receptor. Moreover, we identified lysine 103 of IL-1ß as a target residue suitable for the development of covalent, low-molecular-weight IL-1ß antagonists.

Hydrophobic Surfactant-DNA Complex (Surf-DNA) Enables DNA-Encoded-Library-Compatible Decarboxylative Arylation under Anhydrous Conditions.

DNA-encoded libraries (DELs) are a key technology for identifying small-molecule hits in both the pharmaceutical industry and academia, but their chemical diversity is largely limited to water-compatible reactions to aid in the solubility and integrity of encoding DNA tags. To broaden the DEL chemical space, we present a workflow utilizing DNA-cationic surfactant complexation that enables dissolution and reactions on-DNA in anhydrous organic solvents. We demonstrate its utility by developing DEL-compatible photoredox decarboxylative C(sp2)-C(sp3) coupling under water-free conditions. The workflow is optimized for the 96-well format necessary for large-scale DEL productions, and it enables screening and optimization of DEL-compatible reactions in organic solvents.

[Development of Integrated Database for Experiments and Simulations].

Researchers collect data and use various methods to organize it. Ensuring the reliability and reproducibility of data is crucial, and collaboration across different research fields is on the rise. However, when there is geographical distance, sharing data becomes a challenging task. Therefore, there is a need for the development of a mechanism for sharing data on the web. We have developed an integrated database to facilitate the sharing and management of research data, particularly focusing on small molecules. The integrated database serves as a platform for centralizing data related to small molecules, including their chemical structures, wet lab experimental data, simulation data, and more. It has been constructed as a web application, offering features such as library management for small molecules, registration and viewing of wet lab experiment results, generation of initial conformations for simulations, and data visualization. This enables researchers to efficiently share their research data and collaborate seamlessly, whether within their research group or via cloud-based access that allows project and team members to connect from anywhere. This integrated database plays a critical role in connecting wet lab experiments and simulations, enabling researchers to cross-reference and analyze experimental data comprehensively. It serves as an essential tool to advance research and foster idea generation.

Correlation of protein binding pocket properties with hits' chemistries used in generation of ultra-large virtual libraries.

Although the size of virtual libraries of synthesizable compounds is growing rapidly, we are still enumerating only tiny fractions of the drug-like chemical universe. Our capability to mine these newly generated libraries also lags their growth. That is why fragment-based approaches that utilize on-demand virtual combinatorial libraries are gaining popularity in drug discovery. These à la carte libraries utilize synthetic blocks found to be effective binders in parts of target protein pockets and a variety of reliable chemistries to connect them. There is, however, no data on the potential impact of the chemistries used for making on-demand libraries on the hit rates during virtual screening. There are also no rules to guide in the selection of these synthetic methods for production of custom libraries. We have used the SAVI (Synthetically Accessible Virtual Inventory) library, constructed using 53 reliable reaction types (transforms), to evaluate the impact of these chemistries on docking hit rates for 40 well-characterized protein pockets. The data shows that the virtual hit rates differ significantly for different chemistries with cross coupling reactions such as Sonogashira, Suzuki-Miyaura, Hiyama and Liebeskind-Srogl coupling producing the highest hit rates. Virtual hit rates appear to depend not only on the property of the formed chemical bond but also on the diversity of available building blocks and the scope of the reaction. The data identifies reactions that deserve wider use through increasing the number of corresponding building blocks and suggests the reactions that are more effective for pockets with certain physical and hydrogen bond-forming properties.

Machine learning framework to predict pharmacokinetic profile of small molecule drugs based on chemical structure.

Accurate prediction of a new compound's pharmacokinetic (PK) profile is pivotal for the success of drug discovery programs. An initial assessment of PK in preclinical species and humans is typically performed through allometric scaling and mathematical modeling. These methods use parameters estimated from in vitro or in vivo experiments, which although helpful for an initial estimation, require extensive animal experiments. Furthermore, mathematical models are limited by the mechanistic underpinning of the drugs' absorption, distribution, metabolism, and elimination (ADME) which are largely unknown in the early stages of drug discovery. In this work, we propose a novel methodology in which concentration versus time profile of small molecules in rats is directly predicted by machine learning (ML) using structure-driven molecular properties as input and thus mitigating the need for animal experimentation. The proposed framework initially predicts ADME properties based on molecular structure and then uses them as input to a ML model to predict the PK profile. For the compounds tested, our results demonstrate that PK profiles can be adequately predicted using the proposed algorithm, especially for compounds with Tanimoto score greater than 0.5, the average mean absolute percentage error between predicted PK profile and observed PK profile data was found to be less than 150%. The suggested framework aims to facilitate PK predictions and thus support molecular screening and design earlier in the drug discovery process.

Metabolic Rewiring in Cancer: Small Molecule Inhibitors in Colorectal Cancer Therapy.

Alterations in cellular metabolism, such as dysregulation in glycolysis, lipid metabolism, and glutaminolysis in response to hypoxic and low-nutrient conditions within the tumor microenvironment, are well-recognized hallmarks of cancer. Therefore, understanding the interplay between aerobic glycolysis, lipid metabolism, and glutaminolysis is crucial for developing effective metabolism-based therapies for cancer, particularly in the context of colorectal cancer (CRC). In this regard, the present review explores the complex field of metabolic reprogramming in tumorigenesis and progression, providing insights into the current landscape of small molecule inhibitors targeting tumorigenic metabolic pathways and their implications for CRC treatment.

Small-Molecule Inhibitors of TIPE3 Protein Identified through Deep Learning Suppress Cancer Cell Growth In Vitro.

Tumor necrosis factor-α-induced protein 8-like 3 (TNFAIP8L3 or TIPE3) functions as a transfer protein for lipid second messengers. TIPE3 is highly upregulated in several human cancers and has been established to significantly promote tumor cell proliferation, migration, and invasion and inhibit the apoptosis of cancer cells. Thus, inhibiting the function of TIPE3 is expected to be an effective strategy against cancer. The advancement of artificial intelligence (AI)-driven drug development has recently invigorated research in anti-cancer drug development. In this work, we incorporated DFCNN, Autodock Vina docking, DeepBindBC, MD, and metadynamics to efficiently identify inhibitors of TIPE3 from a ZINC compound dataset. Six potential candidates were selected for further experimental study to validate their anti-tumor activity. Among these, three small-molecule compounds (K784-8160, E745-0011, and 7238-1516) showed significant anti-tumor activity in vitro, leading to reduced tumor cell viability, proliferation, and migration and enhanced apoptotic tumor cell death. Notably, E745-0011 and 7238-1516 exhibited selective cytotoxicity toward tumor cells with high TIPE3 expression while having little or no effect on normal human cells or tumor cells with low TIPE3 expression. A molecular docking analysis further supported their interactions with TIPE3, highlighting hydrophobic interactions and their shared interaction residues and offering insights for designing more effective inhibitors. Taken together, this work demonstrates the feasibility of incorporating deep learning and MD simulations in virtual drug screening and provides inhibitors with significant potential for anti-cancer drug development against TIPE3-.

Perspectives and challenges in developing small molecules targeting purine nucleoside phosphorylase.

As a cytosolic enzyme involved in the purine salvage pathway metabolism, purine nucleoside phosphorylase (PNP) plays an important role in a variety of cellular functions but also in immune system, including cell growth, apoptosis and cancer development and progression. Based on its T-cell targeting profile, PNP is a potential target for the treatment of some malignant T-cell proliferative cancers including lymphoma and leukemia, and some specific immunological diseases. Numerous small-molecule PNP inhibitors have been developed so far. However, only Peldesine, Forodesine and Ulodesine have entered clinical trials and exhibited some potential for the treatment of T-cell leukemia and gout. The most recent direction in PNP inhibitor development has been focused on PNP small-molecule inhibitors with better potency, selectivity, and pharmacokinetic property. In this perspective, considering the structure, biological functions, and disease relevance of PNP, we highlight the recent research progress in PNP small-molecule inhibitor development and discuss prospective strategies for designing additional PNP therapeutic agents.

An ensemble machine learning model generates a focused screening library for the identification of CDK8 inhibitors.

The identification of an effective inhibitor is an important starting step in drug development. Unfortunately, many issues such as the characterization of protein binding sites, the screening library, materials for assays, etc., make drug screening a difficult proposition. As the size of screening libraries increases, more resources will be inefficiently consumed. Thus, new strategies are needed to preprocess and focus a screening library towards a targeted protein. Herein, we report an ensemble machine learning (ML) model to generate a CDK8-focused screening library. The ensemble model consists of six different algorithms optimized for CDK8 inhibitor classification. The models were trained using a CDK8-specific fragment library along with molecules containing CDK8 activity. The optimized ensemble model processed a commercial library containing 1.6 million molecules. This resulted in a CDK8-focused screening library containing 1,672 molecules, a reduction of more than 99.90%. The CDK8-focused library was then subjected to molecular docking, and 25 candidate compounds were selected. Enzymatic assays confirmed six CDK8 inhibitors, with one compound producing an IC50 value of ≤100 nM. Analysis of the ensemble ML model reveals the role of the CDK8 fragment library during training. Structural analysis of molecules reveals the hit compounds to be structurally novel CDK8 inhibitors. Together, the results highlight a pipeline for curating a focused library for a specific protein target, such as CDK8.

Identification of potent pan-ephrin receptor kinase inhibitors using DNA-encoded chemistry technology.

EPH receptors (EPHs), the largest family of tyrosine kinases, phosphorylate downstream substrates upon binding of ephrin cell surface-associated ligands. In a large cohort of endometriotic lesions from individuals with endometriosis, we found that EPHA2 and EPHA4 expressions are increased in endometriotic lesions relative to normal eutopic endometrium. Because signaling through EPHs is associated with increased cell migration and invasion, we hypothesized that chemical inhibition of EPHA2/4 could have therapeutic value. We screened DNA-encoded chemical libraries (DECL) to rapidly identify EPHA2/4 kinase inhibitors. Hit compound, CDD-2693, exhibited picomolar/nanomolar kinase activity against EPHA2 (Ki: 4.0 nM) and EPHA4 (Ki: 0.81 nM). Kinome profiling revealed that CDD-2693 bound to most EPH family and SRC family kinases. Using NanoBRET target engagement assays, CDD-2693 had nanomolar activity versus EPHA2 (IC50: 461 nM) and EPHA4 (IC50: 40 nM) but was a micromolar inhibitor of SRC, YES, and FGR. Chemical optimization produced CDD-3167, having picomolar biochemical activity toward EPHA2 (Ki: 0.13 nM) and EPHA4 (Ki: 0.38 nM) with excellent cell-based potency EPHA2 (IC50: 8.0 nM) and EPHA4 (IC50: 2.3 nM). Moreover, CDD-3167 maintained superior off-target cellular selectivity. In 12Z endometriotic epithelial cells, CDD-2693 and CDD-3167 significantly decreased EFNA5 (ligand) induced phosphorylation of EPHA2/4, decreased 12Z cell viability, and decreased IL-1ß-mediated expression of prostaglandin synthase 2 (PTGS2). CDD-2693 and CDD-3167 decreased expansion of primary endometrial epithelial organoids from patients with endometriosis and decreased Ewing's sarcoma viability. Thus, using DECL, we identified potent pan-EPH inhibitors that show specificity and activity in cellular models of endometriosis and cancer.

Using DNA-encoded libraries of fragments for hit discovery of challenging therapeutic targets.

INTRODUCTION: The effectiveness of Fragment-based drug design (FBDD) for targeting challenging therapeutic targets has been hindered by two factors: the small library size and the complexity of the fragment-to-hit optimization process. The DNA-encoded library (DEL) technology offers a compelling and robust high-throughput selection approach to potentially address these limitations. AREA COVERED: In this review, the authors propose the viewpoint that the DEL technology matches perfectly with the concept of FBDD to facilitate hit discovery. They begin by analyzing the technical limitations of FBDD from a medicinal chemistry perspective and explain why DEL may offer potential solutions to these limitations. Subsequently, they elaborate in detail on how the integration of DEL with FBDD works. In addition, they present case studies involving both de novo hit discovery and full ligand discovery, especially for challenging therapeutic targets harboring broad drug-target interfaces. EXPERT OPINION: The future of DEL-based fragment discovery may be promoted by both technical advances and application scopes. From the technical aspect, expanding the chemical diversity of DEL will be essential to achieve success in fragment-based drug discovery. From the application scope side, DEL-based fragment discovery holds promise for tackling a series of challenging targets.

Discovery of potent small-molecule inhibitors of lipoprotein(a) formation.

Lipoprotein(a) (Lp(a)), an independent, causal cardiovascular risk factor, is a lipoprotein particle that is formed by the interaction of a low-density lipoprotein (LDL) particle and apolipoprotein(a) (apo(a))1,2. Apo(a) first binds to lysine residues of apolipoprotein B-100 (apoB-100) on LDL through the Kringle IV (KIV) 7 and 8 domains, before a disulfide bond forms between apo(a) and apoB-100 to create Lp(a) (refs. 3-7). Here we show that the first step of Lp(a) formation can be inhibited through small-molecule interactions with apo(a) KIV7-8. We identify compounds that bind to apo(a) KIV7-8, and, through chemical optimization and further application of multivalency, we create compounds with subnanomolar potency that inhibit the formation of Lp(a). Oral doses of prototype compounds and a potent, multivalent disruptor, LY3473329 (muvalaplin), reduced the levels of Lp(a) in transgenic mice and in cynomolgus monkeys. Although multivalent molecules bind to the Kringle domains of rat plasminogen and reduce plasmin activity, species-selective differences in plasminogen sequences suggest that inhibitor molecules will reduce the levels of Lp(a), but not those of plasminogen, in humans. These data support the clinical development of LY3473329-which is already in phase 2 studies-as a potent and specific orally administered agent for reducing the levels of Lp(a).