Discovery of a selective and biologically active low-molecular weight antagonist of human interleukin-1β

At The Interface: Small-Molecule Inhibitors of Soluble Cytokines

Cytokines are crucial regulators of the immune system that orchestrate interactions between cells and, when dysregulated, contribute to the progression of chronic inflammation, cancer, and autoimmunity. Numerous biologic-based clinical agents, mostly monoclonal antibodies, have validated cytokines as important clinical targets and are now part of the standard of care for a number of diseases. These agents, while impactful, still suffer from limitations including a lack of oral bioavailability, high cost of production, and immunogenicity. Small-molecule cytokine inhibitors are attractive alternatives that can address these limitations. Although targeting cytokine-cytokine receptor complexes with small molecules has been a challenging research endeavor, multiple small-molecule inhibitors have now been identified, with a number of them undergoing clinical evaluation. In this review, we highlight the recent advancements in the discovery and development of small-molecule inhibitors targeting soluble cytokines. The strategies for identifying these novel ligands as well as the structural and mechanistic insights into their activity represent important milestones in tackling these challenging and clinically important protein-protein interactions.

Computational advances in discovering cryptic pockets for drug discovery

A number of promising therapeutic target proteins have been considered "undruggable" due to the lack of well-defined ligandable pockets. Substantial research in protein dynamics has elucidated the existence of "cryptic" pockets that only exist transiently and become favorable for binding in the presence of a ligand. These pockets provide an avenue to target challenging proteins, inspiring the development of multiple computational methods. This review highlights established cryptic pocket modeling approaches like mixed solvent molecular dynamics and recent applications of enhanced sampling and AI-based methods in therapeutically relevant proteins.

Fragment-to-Lead Medicinal Chemistry Publications in 2023

This Perspective summarizes successful fragment-to-lead (F2L) studies that were published in 2023 and is the ninth installment in an annual series. A tabulated summary of the relevant articles published in 2023 is provided (17 entries from 16 articles), and a comparison of the target classes, screening methods, and overall fragment or lead property trends for 2023 examples and for the combined entries over the years 2015-2023 is discussed. In addition, we identify several trends and innovations in the 2023 literature that promise to further increase the success of fragment-based drug discovery (FBDD), particularly in the areas of NMR and virtual screening, fragment library design, and fragment linking.

Fluorine Labeling and 19F NMR Spectroscopy to Study Biological Molecules and Molecular Complexes

High-resolution nuclear magnetic resonance (NMR) spectroscopy represents a key methodology for studying biomolecules and their interplay with other molecules. Recent developments in labeling strategies have made it possible to incorporate fluorine into proteins and peptides reliably, with manageable efforts and, importantly, in a highly site-specific manner. Paired with its excellent NMR spectroscopic properties and absence in most biological systems, fluorine has enabled scientists to investigate a rather wide range of scientific objectives, including protein folding, protein dynamics and drug discovery. Furthermore, NMR spectroscopic experiments can be conducted in complex environments, such as cell lysate or directly inside living cells. This review presents selected studies demonstrating how 19F NMR spectroscopic approaches enable to contribute to the understanding of biomolecular processes. Thereby the focus has been set to labeling strategies available and specific NMR experiments performed to answer the underlying scientific objective.

Advancing Target Validation and Ligandability Assessment: Fragment Screening via 19F NMR Spectroscopy

In drug discovery, precise target validation and ligandability assessment are pivotal processes. This article introduces an advanced protocol employing fluorine-19 (19F) NMR spectroscopy for fragment-based screening (FBS). This innovative methodology distinguishes fragment hits and elucidates their binding interactions, even within millimolar concentration ranges. Leveraging the distinctive advantages of 19F NMR, such as high signal dispersion, background-free spectra, and heightened sensitivity to protein binding, this technique emerges as a potent tool for early-stage drug discovery. The demonstrated application of this protocol in target validation, with a specific emphasis on ligandability assessment, provides a means to gauge the inherent challenges associated with a target right from the outset of a project.

Inflammasome components as new therapeutic targets in inflammatory disease

Inflammation drives pathology in many human diseases for which there are no disease-modifying drugs. Inflammasomes are signalling platforms that can induce pathological inflammation and tissue damage, having potential as an exciting new class of drug targets. Small-molecule inhibitors of the NLRP3 inflammasome that are now in clinical trials have demonstrated proof of concept that inflammasomes are druggable, and so drug development programmes are now focusing on other key inflammasome molecules. In this Review, we describe the potential of inflammasome components as candidate drug targets and the novel inflammasome inhibitors that are being developed. We discuss how the signalling biology of inflammasomes offers mechanistic insights for therapeutic targeting. We also discuss the major scientific and technical challenges associated with drugging these molecules during preclinical development and clinical trials.

Small molecule inhibitors of IL-1R1/IL-1β interaction identified via transfer machine learning QSAR modelling

The human interleukin-1 receptor I (IL-1R1) is a cytokine receptor recognized by interleukin 1β (IL-1β), among other cytokines. Over activation of IL-1R1 has been implicated in various inflammatory conditions. This research aims to identify small-molecule inhibitors targeting the hIL1R1/IL1β interaction, employing a multi-task transfer learning approach for quantitative structure-activity relationship (QSAR) modelling. A comprehensive bioactivity dataset from functionally related proteins was utilised to build a robust ensemble machine learning model for predicting IC50 values against the target protein. Despite the availability of antibody-based therapies, the absence of orally available small-molecule inhibitors necessitates their development. By combining model predictions with docking and simulation approaches, the interleukin-1 receptor inhibitor (IRI-1) emerged as a lead compound. It potently inhibited human IL1-R1 with micromolar activity in THP-1 and Saos-2 cells and demonstrated good biocompatibility. Western blot analysis revealed that IRI-1 inhibits IL-1β-mediated phosphorylation of IL1-R1, JNK, IRAK-4, and ERK in THP-1 cells. Furthermore, molecular dynamics simulations confirmed the structural stability of the protein-ligand complexes. This study highlights the effectiveness of multi-task transfer learning approaches for building robust QSAR models against novel proteins or those with limited bioactivity data, such as hIL-1β/IL-1R1 protein.

In-vitro anti-acne activity of Teucrium oliverianum methanolic extract against Cutibacterium acnes

Background

Acne vulgaris is a skin infection widely seen in adolescents between 10-19 years with males affected more than females. It mainly affects the face but may also affect the back and chest. The symptoms vary with mild acne manifesting as comedones and moderate acne as inflammatory lesions (papulopustular), nodules, and mild scarring while severe acne has the same symptoms that have not subsided within 6 months of treatment. Various treatments including topical medications containing different antibiotics are used to treat acne. Recently, herbal treatments have been shown as better alternatives to conventional treatment. Teucrium oliverianum Ging. ex Benth (Lamiaceae) is traditionally used for skin infections such as wound healing and biofilm formation.

Methodology

Methanolic extract of T. oliverianum was subjected to liquid chromatography-mass spectrometry (LC-MS) analysis, and its antibacterial effect against Cutibacterium acnes. The anti-acne, anti-inflammatory, and antioxidant effects were also assessed using HaCaT cells infected with C. acnes. The cytotoxicity of the extract was evaluated using a neutral red uptake assay, and anti-inflammatory effects were determined by measuring TNF-α, IL-1β, INF-γ, and COX2 inhibition. The antioxidant action was assessed by ROS generation in HaCaT cells infected with C. acnes.

Results

LC-MS analysis of the extract showed the presence of 16 different metabolites with L-carnitine, esculin sesquihydrate, and gamma-linoleic acid as major metabolites. The methanolic extract of T. oliverianum showed an antibacterial effect against C. acnes with an IC50 value of 263.2 μg/mL. The extract attenuated the cytotoxicity of C. acnes on the HaCaT cell and the IC50 was found to be 676.2 μg/mL. It also decreased dose-dependently the expression of TNF-α, IL-1β, INF-γ, and inhibited COX2 in the HaCaT cells infected with C. acnes. It also decreased the generation of reactive oxygen species.

Conclusion

The results support the use of T. oliverianum as an anti-acne agent but it possesses mild antibacterial action. It showed anti-inflammatory effects in HaCaT cells infected with C. acnes. It is also an effective antioxidant and decreased the generation of reactive oxygen species. Comparison of the anti-acne effects and adverse reactions of extract with other treatments will provide more insight into its clinical efficacy and toxicity.

Toward Unveiling Putative Binding Sites of Interleukin-33: Insights from Mixed-Solvent Molecular Dynamics Simulations of the Interleukin-1 Family

The interleukin (IL)-1 family is a major proinflammatory cytokine family, ranging from the well-studied IL-1s to the most recently discovered IL-33. As a new focus, IL-33 has attracted extensive research for its crucial immunoregulatory roles, leading to the development of notable monoclonal antibodies as clinical candidates. Efforts to develop small molecules disrupting IL-33/ST2 interaction remain highly desired but encounter challenges due to the shallow and featureless interfaces. The information from relative cytokines has shown that traditional binding site identification methods still struggle in mapping cryptic sites, necessitating dynamic approaches to uncover druggable pockets on IL-33. Here, we employed mixed-solvent molecular dynamics (MixMD) simulations with diverse-property probes to map the hotspots of IL-33 and identify potential binding sites. The protocol was first validated using the known binding sites of two IL-1 family members and then applied to the structure of IL-33. Our simulations revealed several binding sites and proposed side-chain rearrangements essential for the binding of a known inhibitor, aligning well with experimental NMR findings. Further microsecond-time scale simulations of this IL-33-protein complex unveiled distinct binding modes with varying occurrences. These results could facilitate future efforts in developing ligands to target challenging flexible pockets of IL-33 and IL-1 family cytokines in general.

Current perspectives in the management of sepsis and septic shock

Within patients with sepsis, there exists significant heterogeneity, and while all patients should receive conventional therapy, there are subgroups of patients who may benefit from specific therapies, often referred to as rescue therapies. Therefore, the identification of these specific patient subgroups is crucial and lays the groundwork for the application of precision medicine based on the development of targeted interventions. Over the years, efforts have been made to categorize sepsis into different subtypes based on clinical characteristics, biomarkers, or underlying mechanisms. For example, sepsis can be stratified into different phenotypes based on the predominant dysregulated host response. These phenotypes can range from hyperinflammatory states to immunosuppressive states and even mixed phenotypes. Each phenotype may require different therapeutic approaches to improve patient outcomes. Rescue strategies for septic shock may encompass various interventions, such as immunomodulatory therapies, extracorporeal support (e.g., ECMO), or therapies targeted at specific molecular or cellular pathways involved in the pathophysiology of sepsis. In recent years, there has been growing interest in precision medicine approaches to sepsis and phenotype identification. Precision medicine aims to tailor treatments to each individual patient based on their unique characteristics and disease mechanisms.

Ligandability Assessment of IL-1β by Integrated Hit Identification Approaches

Human interleukin-1β (IL-1β) 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-1β binders that interfere with IL-1β 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-1β, all of them also inhibiting the interaction with the IL-1R1 receptor. Moreover, we identified lysine 103 of IL-1β as a target residue suitable for the development of covalent, low-molecular-weight IL-1β antagonists.

Exploring the Wound Healing Potential of a Cuscuta chinensis Extract-Loaded Nanoemulsion-Based Gel

Cuscuta chinensis (C. chinensis) presents many pharmacological activities, including antidiabetic effects, and antioxidant, anti-inflammatory, and antitumor properties. However, the wound care properties of this plant have not yet been reported. Therefore, this research aimed to evaluate the antioxidant, anti-inflammatory, and antibacterial activities of ethanol and ethyl acetate C. chinensis extracts. The phytochemical markers in the extracts were analyzed using high-performance liquid chromatography (HPLC). Then, the selected C. chinensis extract was developed into a nanoemulsion-based gel for wound care testing in rats. The results showed that both of the C. chinensis extracts exhibited antioxidant activity when tested using 2,2-Diphenyl-1-picrylhydrazyl (DPPH), ferric reducing antioxidant power (FRAP), and lipid peroxidation inhibition assays. They reduced the expression of IL-1β, IL-6, and TNF-α in RAW264.7 cells induced with lipopolysaccharide (LPS). The ethyl acetate extract also had antibacterial properties. Kaempferol was found in both extracts, whereas hyperoside was found only in the ethanol extract. These compounds were found to be related to the biological activities of the extracts, confirmed via molecular docking. The C. chinensis extract-loaded nanoemulsions had a small particle size, a narrow polydispersity index (PDI), and good stability. Furthermore, the C. chinensis extract-loaded nanoemulsion-based gel had a positive effect on wound healing, presenting a better percentage wound contraction Fucidin cream. In conclusion, this formulation has the potential for use as an alternative wound treatment and warrants further study in clinical trials.

SWISH-X, an Expanded Approach to Detect Cryptic Pockets in Proteins and at Protein-Protein Interfaces

Protein-protein interactions mediate most molecular processes in the cell, offering a significant opportunity to expand the set of known druggable targets. Unfortunately, targeting these interactions can be challenging due to their typically flat and featureless interaction surfaces, which often change as the complex forms. Such surface changes may reveal hidden (cryptic) druggable pockets. Here, we analyze a set of well-characterized protein-protein interactions harboring cryptic pockets and investigate the predictive power of current computational methods. Based on our observations, we developed a new computational strategy, SWISH-X (SWISH Expanded), which combines the established cryptic pocket identification capabilities of SWISH with the rapid temperature range exploration of OPES MultiThermal. SWISH-X is able to reliably identify cryptic pockets at protein-protein interfaces while retaining its predictive power for revealing cryptic pockets in isolated proteins, such as TEM-1 β-lactamase.