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Liver fibrosis is an urgent clinical problem without effective therapies. Here we conducted a high-content screening on a natural Euphorbiaceae diterpenoid library to identify a potent anti-liver fibrosis lead, 12-deoxyphorbol 13-palmitate (DP). Leveraging a photo-affinity labeling approach, apolipoprotein L2 (APOL2), an endoplasmic reticulum (ER)-rich protein, was identified as the direct target of DP. Mechanistically, APOL2 is induced in activated hepatic stellate cells upon transforming growth factor-β1 (TGF-β1) stimulation, which then binds to sarcoplasmic/ER calcium ATPase 2 (SERCA2) to trigger ER stress and elevate its downstream protein kinase R-like ER kinase (PERK)-hairy and enhancer of split 1 (HES1) axis, ultimately promoting liver fibrosis. As a result, targeting APOL2 by DP or ablation of APOL2 significantly impairs APOL2-SERCA2-PERK-HES1 signaling and mitigates fibrosis progression. Our findings not only define APOL2 as a novel therapeutic target for liver fibrosis but also highlight DP as a promising lead for treatment of this symptom., Competing Interests: Competing interests: The authors declare no conflicts of interest., (© 2024. The Author(s), under exclusive licence to Springer Nature America, Inc.)

The landscape of cancer treatment has been transformed by the emergence of immunotherapy, especially through the use of antibodies that target the PD-1/PD-L1 pathway. Recently, there has been a notable increase in interest surrounding immune checkpoint inhibitors for cancer therapy. While antibody-based approaches have drawbacks like high costs and prolonged activity, the approval of monoclonal antibodies such as pembrolizumab and nivolumab has paved the way for a range of alternative therapies, including peptides, peptidomimetics, and small-molecule inhibitors. These smaller molecules, which target the PD-1/PD-L1 interaction, are seen as potential substitutes or supplements to monoclonal antibodies. Our focus in this article is primarily on exploring small molecules designed for PD-1/PD-L1 checkpoint pathway modulation in cancer immunotherapy, along with highlighting current advances in their structural and preclinical/clinical development. The pursuit of therapeutics based on small-molecule inhibitors of the PD-1/PD-L1 axis offers a promising yet intricate avenue for advancing cancer treatment., Competing Interests: Declaration of competing interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (Copyright © 2024 Elsevier Inc. All rights reserved.)

Monoamine oxidase B (MAO-B) is a key enzyme in the mitochondrial outer membrane, pivotal for the oxidative deamination of biogenic amines. Its overexpression has been implicated in the pathogenesis of several cancers, including glioblastoma and colorectal, lung, renal, and bladder cancers, primarily through the increased production of reactive oxygen species (ROS). Inhibition of MAO-B impedes cell proliferation, making it a potential therapeutic target. Various monoamine oxidase B inhibitors have shown promise in inhibiting tumor growth and inducing apoptosis across different cancer types. In this review, we investigate MAO-B network biology, which highlighted glycolysis pathways as notable links between MAO-B and cancer. Further molecular modeling analysis illustrated the basis of MAO-B ligand binding, revealing a hydrophobic binding pocket, with key residues such as Tyr398 and Tyr435 playing crucial roles in substrate oxidation. MAO-B inhibitors that were reportsed in the literature (2012-2024) and their potential application in cancer therapy were discussed, highlighting key molecular scaffolds, such as propargyl analogs of phenyl alkyl amines, hydrazine derivatives, cyclopropylamine derivatives, MAO-B activated pro-drugs, and natural phenylpropanoid derivatives. The reported literature underscores the therapeutic potential of MAO-B inhibitors as versatile anticancer agents, warranting further investigation to optimize their efficacy and specificity across various malignancies.

Hematopoietic progenitor kinase 1 (HPK1/MAP4K1) represents a high interest target for the treatment of cancer through an immune-mediated mechanism. Herein we present highlights of the drug discovery campaign within the lactam/azalactam series of inhibitors that yielded a small molecule ( 21 , PF-07265028), which was advanced to a phase 1 clinical trial (NCT05233436). Key components of the discovery effort included optimization of potency through mitigation of ligand strain as guided by the use of cocrystal structures, mitigation of ADME liabilities (plasma instability and fraction metabolism by CYP2D6), and optimization of kinase selectivity, particularly over immune-modulating kinases with high homology to HPK1. Structure-based drug design via leveraging cocrystal structures and lipophilic efficiency analysis proved to be valuable tools that ultimately enabled the delivery of a clinical-quality small molecule inhibitor of HPK1.

Abnormal phosphorylation of proteins can lead to various diseases, particularly cancer. Therefore, the development of small molecules for precise regulation of protein phosphorylation holds great potential for drug design. While the traditional kinase/phosphatase small-molecule modulators have shown some success, achieving precise phosphorylation regulation has proven to be challenging. The emergence of heterobifunctional molecules, such as phosphorylation-inducing chimeric small molecules (PHICSs) and phosphatase recruiting chimeras (PHORCs), with proximity-inducing modalities is expected to lead to a breakthrough by specifically recruiting kinase or phosphatase to the protein of interest. Herein, we summarize the drug targets with aberrant phosphorylation in cancer and underscore the potential of correcting phosphorylation in cancer therapy. Through reported cases of heterobifunctional molecules targeting phosphorylation regulation, we highlight the current design strategies and features of these molecules. We also provide a systematic elaboration of the link between aberrantly phosphorylated targets and cancer as well as the existing challenges and future research directions for developing heterobifunctional molecular drugs for phosphorylation regulation.

Small molecule antioxidants can inhibit or retard oxidation reactions and protect against free radical damage to cells, thus playing a key role in food, cosmetics, pharmaceuticals, the environment, as well as materials. Experimentally driven antioxidant discovery is a major paradigm, and computationally assisted antioxidants are rarely reported. In this study, a functional-group-based alternating multitask self-supervised molecular representation learning method is proposed to simultaneously predict the antioxidant activities of small molecules for eight commonly used in vitro antioxidant assays. Extensive evaluation results reveal that compared with the baseline models, the multitask FG-BERT model achieves the best overall predictive performance, with the highest average F1, BA, ROC-AUC, and PRC-AUC values of 0.860, 0.880, 0.954, and 0.937 for the test sets, respectively. The Y-scrambling testing results further demonstrate that such a deep learning model was not constructed by accident and that it has reliable predictive capabilities. Additionally, the excellent interpretability of the multitask FG-BERT model makes it easy to identify key structural fragments/groups that contribute significantly to the antioxidant effect of a given molecule. Finally, an online antioxidant activity prediction platform called AOP (freely available at https://aop.idruglab.cn/) and its local version were developed based on the high-quality multitask FG-BERT model for experts and nonexperts in the field. We anticipate that it will contribute to the discovery of novel small-molecule antioxidants.

Pleckstrin homology (PH) domains are structural motifs critical for cellular processes, such as signal transduction and cytoskeletal organization. Due to their involvement in various diseases, PH domains are promising therapeutic targets, yet their highly charged and hydrophobic binding sites are not ideal for traditional small drugs. In this study, we designed a DNA-encoded library (DEL) mimicking phospholipids to identify novel modulators targeting PH domains with uncharted chemical properties. Screening against several PH domains led to the discovery of 2DII, a small molecule that selectively binds to mSin1 PH . This compound can modulate mTORC2 activity by impairing mTORC2's membrane interactions, resulting in reduced AKT1 phosphorylation. A micromapping via Dexter energy transfer based on 2DII bearing an iridium catalyst (2DII-Ir), along with a biotin-diazirine small molecule was used for target identification by proteomics, which confirmed mSin1 as the primary intracellular target of 2DII, demonstrating its potential for selective mTORC2 pathway modulation. These findings introduce a novel strategy for targeting PH domains and provide a foundation for the development of therapeutic interventions that modulate PH-domain-dependent signaling pathways.

Connecting two small molecules, such as ligands, fluorophores, or lipids, together via a linker with amide bonds is a widely used strategy to generate synthetic bifunctional molecules for various biological and biomedical applications. Such bifunctional molecules have been used in live-cell experiments under the assumption that they should be stable in cells. However, we recently found that a membrane-targeting bifunctional molecule, composed of a lipopeptide and the small-molecule ligand trimethoprim, referred to as mgcTMP, underwent amide-bond cleavage in mammalian cells. In this work, we first identified γ-secretase as the major protease degrading mgcTMP in cells. We next investigated the intracellular degradation of several different types of amide-linked bifunctional compounds and found that N -terminally fatty acid-conjugated small molecules are susceptible to γ-secretase-mediated amide-bond cleavage. In contrast, amide-linked bifunctional molecules composed of two small molecules, such as ligands and hydrophobic groups, which lack lipid modification, did not undergo intracellular degradation. These findings highlight a previously overlooked consideration for the development and application of lipid-based bifunctional molecules in chemical biology research.

Chemical proteomics enables the global analysis of small molecule-protein interactions in native biological systems and has emerged as a versatile approach for ligand discovery. The range of small molecules explored by chemical proteomics has, however, remained limited. Here, we describe a diversity-oriented synthesis (DOS)-inspired library of stereochemically defined compounds bearing diazirine and alkyne units for UV light-induced covalent modification and click chemistry enrichment of interacting proteins, respectively. We find that these "photo-stereoprobes" interact in a stereoselective manner with hundreds of proteins from various structural and functional classes in human cells and demonstrate that these interactions can form the basis for high-throughput screening-compatible NanoBRET assays. Integrated phenotypic screening and chemical proteomics identified photo-stereoprobes that modulate autophagy by engaging the mitochondrial serine protease CLPP. Our findings show the utility of DOS-inspired photo-stereoprobes for expanding the ligandable proteome, furnishing target engagement assays, and facilitating the discovery and characterization of bioactive compounds in phenotypic screens., Competing Interests: Declaration of interests The authors declare no competing interests., (Copyright © 2024 Elsevier Ltd. All rights reserved.)

Automated batch and flow setups are well-established for high throughput experimentation in both thermal chemistry and photochemistry. However, the development of automated electrochemical platforms is hindered by cell miniaturization challenges in batch and difficulties in designing effective single-pass flow systems. In order to address these issues, we have designed and implemented a new, slug-based automated electrochemical flow platform. This platform was successfully demonstrated for electrochemical C-N cross-couplings of E3 ligase binders with diverse amines (44 examples), which were subsequently transferred to a continuous-flow mode for confirmation and isolation, showing its applicability for medicinal chemistry purposes. To further validate the versatility of the platform, Design of Experiments (DoE) optimization was performed for an unsuccessful library target. This optimization process, fully automated by the platform, resulted in a remarkable 6-fold increase in reaction yield., (© 2024 The Authors. Angewandte Chemie International Edition published by Wiley-VCH GmbH.)

Fragment-based drug discovery (FBDD) is a crucial strategy for developing new drugs that have been applied to diverse targets, from neglected infectious diseases to cancer. With at least seven drugs already launched to the market, this approach has gained interest in both academics and industry in the last 20 years. FBDD relies on screening small libraries with about 1000-2000 compounds of low molecular weight (about 300 Da) using several biophysical methods. Because of the reduced size of the compounds, the chemical space and diversity can be better explored than large libraries used in high throughput screenings. This review summarises the most common biophysical techniques used in fragment screening and orthogonal validation. We also explore the advantages and drawbacks of the different biophysical techniques and examples of applications and strategies., (© 2024 Wiley-VCH GmbH.)

Targeted protein degradation (TPD) technologies, particularly proteolysis targeting chimeras (PROTACs), have emerged as a promising branch of targeted therapy. Current ubiquitin-proteasome-dependent TPD technologies are limited to targeting intracellular proteins. Although the blockade of immune checkpoints has achieved great clinical success, most immune checkpoints are transmembrane proteins, which are difficult to be ubiquitinated and degraded by PROTACs. Herein, we developed a novel discovery strategy of bifunctional small molecules, which could mediate autophagy-lysosome degradation of immune checkpoints. F-1 was demonstrated to activate the autophagy-lysosome system, and conjugation of F-1 with inhibitors targeting programmed cell death-ligand 1 (PD-L1) or V-domain Ig suppressor of T-cell activation (VISTA) generated a new class of small molecules that effectively induce the degradation of PD-L1 or VISTA in tumor cells. The most promising PD-L1 degrader B3 significantly induced PD-L1 degradation in RKO cells through the autophagy-lysosome system and exhibited good tumor-inhibiting effects in vivo. Our work could expand the development of degraders targeting immune checkpoints and provide a promising discovery strategy for future autophagy-lysosome targeting degradation technology., Competing Interests: Declaration of competing interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (Copyright © 2024 Elsevier Masson SAS. All rights reserved.)

Neprilysin is a cell surface metallo-endopeptidase, commonly identified as neutral endopeptidase (NEP), that plays a crucial role in the cleavage of peptides, for example, natriuretic peptides, angiotensin II, enkephalins, endothelin, bradykinin, substance P, glucagon-like peptide and amyloid beta. In the case of heart failure, a significant upsurge in NEP activity and expression enhances the degradation of natriuretic peptides. Therefore, NEP inhibitors have gained attention in the field of cardiology. NEP has been studied for over 40 years; however, it has recently gained attention with the US FDA approval of a fixed dose combination of sacubitril (NEP inhibitor) and valsartan (AT-1 inhibitor) for chronic heart failure treatment. The present review elucidates the role of neprilysin in cardiorenal disease, its pathophysiology, and how NEP inhibition benefits. It also summarizes the research advances in NEP inhibitors (NEPi) and their structure-activity relationships. Moreover, the review provides insight into NEPi effectiveness - alone or combined with other cardiorenal protective agents. It is expected to help medicinal chemists synthesize and develop novel NEPi., Competing Interests: Declaration of competing interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (Copyright © 2024 Elsevier Masson SAS. All rights reserved.)

Inhibition of endoplasmic reticulum aminopeptidase 1 (ERAP1) by small-molecules is being eagerly investigated for the treatment of various autoimmune diseases and in the field of immuno-oncology after its active involvement in antigen presentation and processing. Currently, ERAP1 inhibitors are at different stages of clinical development, which highlights its significance as a promising drug target. In the present work, we describe the first-ever successful identification of several ERAP1 inhibitors derived from a fragment-based screening approach. We applied an enzymatic activity assay to a large library of ∼3000 fragment entries in order to retrieve 32 hits. After a multi-faceted selection process, we prioritized 3 chemotypes for SAR optimization and strategic modifications provided 2 series (2-thienylacetic acid and rhodanine scaffolds) with improved analogues at the low micromolar range of ERAP1 inhibition. We report also evidence of selectivity against homologous aminopeptidase IRAP, combined with complementary in silico docking studies to predict the binding mode and site of inhibition. Our compounds can be the starting point for future fragment growing and rational drug development, incorporating new chemical modalities., Competing Interests: Declaration of competing interest The authors declare the following financial interests/personal relationships which may be considered as potential competing interests: Rebecca Deprez-Poulain reports financial support was provided by European Commission. Vasileios Fougiaxis reports financial support was provided by European Commission. Rebecca Deprez-Poulain reports financial support was provided by French National Research Agency. If there are other authors, they declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (Copyright © 2024 The Authors. Published by Elsevier Masson SAS.. All rights reserved.)

Schistosomiasis is a tropical disease that poses a significant risk to hundreds of millions of people, yet often goes unnoticed. While praziquantel, a widely used anti-schistosome drug, has a low cost and a high cure rate, it has several drawbacks. These include ineffectiveness against schistosome larvae, reduced efficacy in young children, and emerging drug resistance. Discovering new and active anti-schistosome small molecules is therefore critical, but this process presents the challenge of low accuracy in computer-aided methods. To address this issue, we proposed GNN-DDAS, a novel deep learning framework based on graph neural networks (GNN), designed for drug discovery to identify active anti-schistosome (DDAS) small molecules. Initially, a multi-layer perceptron was used to derive sequence features from various representations of small molecule SMILES. Next, GNN was employed to extract structural features from molecular graphs. Finally, the extracted sequence and structural features were then concatenated and fed into a fully connected network to predict active anti-schistosome small molecules. Experimental results showed that GNN-DDAS exhibited superior performance compared to the benchmark methods on both benchmark and real-world application datasets. Additionally, the use of GNNExplainer model allowed us to analyze the key substructure features of small molecules, providing insight into the effectiveness of GNN-DDAS. Overall, GNN-DDAS provided a promising solution for discovering new and active anti-schistosome small molecules., (© 2024 Wiley Periodicals LLC.)

Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), which causes coronavirus disease 2019 (COVID-19), has not yet been eradicated. SARS-CoV-2 has two types of proteases, a main protease (M pro ) and a papain-like protease (PL pro ), which together process two translated non-structural polyproteins, pp1a and pp1ab, to produce functional viral proteins. In this study, effective inhibitors against PL pro of SARS-CoV-2 were designed and synthesized using GRL-0048 as a lead. A docking simulation of GRL-0048 and SARS-CoV-2 PL pro showed that GRL-0048 noncovalently interacts with PL pro , and there is a newly identified binding pocket in PL pro . Structure-activity relationship studies were next performed on GRL-0048, resulting in the development of several inhibitors, specifically compounds 1, 2b, and 3h, that have more potent inhibitory activity than GRL-0048., Competing Interests: Declaration of competing interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (Copyright © 2024 The Authors. Published by Elsevier Masson SAS.. All rights reserved.)

Membrane-bound enzymes play pivotal roles in various cellular processes, making their activity regulation essential for cellular homeostasis and signaling transduction. Given that dysregulation of membrane-bound enzymes involved in various disease, controlling enzyme activity offers valuable avenues for designing targeted therapies and novel pharmaceutical interventions. This review explores chemical stimuli-responsive strategies for modulating the activity of these enzymes, employing diverse stimuli such as small molecules, proteins, nucleic acids, and bifunctional molecules to either inhibit or enhance their catalytic function. We systematically delineate the mechanisms underlying enzyme activity regulation, including substrate binding site blockade, conformational changes, and local concentration of enzymes and substrates. Furthermore, based on some examples, we elucidate the binding modalities between stimuli and enzymes, along with potential modes of regulation, and discuss their potential medical applications and future prospects. This review underscores the significance of understanding and manipulating enzyme activity on the cell membrane for advancing biomedical research and therapeutic development., Competing Interests: Declaration of competing interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (Copyright © 2024 Elsevier Masson SAS. All rights reserved.)

Toll-like receptor 4 (TLR4) plays a vital role in the innate immune response, but its overactivation has been associated with several diseases, such as aggressive progression of triple-negative breast cancer (TNBC). As a result, inhibiting TLR4 has emerged as a potential therapeutic strategy for this challenging breast cancer subtype. This review summarizes recent advancements in the development of small-molecule TLR4 antagonists to suppress TNBC growth, metastasis, and chemotherapy resistance. We also examine their potential in managing cancer-related complications and propose future directions for their application in TNBC therapy., Competing Interests: Declaration of competing interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (Copyright © 2024 Elsevier Ltd. All rights reserved.)

Fibroblast activation protein (FAP) has been extensively studied as a cancer biomarker for decades. Recently, small-molecule FAP inhibitors have been widely adopted as a targeting moiety of experimental theranostic radiotracers. Here we present a fast qPCR-based analytical method allowing FAP inhibition screening in a high-throughput regime. To identify clinically relevant compounds that might interfere with FAP-targeted approaches, we focused on a library of FDA-approved drugs. Using the DNA-linked Inhibitor Antibody Assay (DIANA), we tested a library of 2667 compounds within just a few hours and identified numerous FDA-approved drugs as novel FAP inhibitors. Among these, prodrugs of cephalosporin antibiotics and reverse transcriptase inhibitors, along with one elastase inhibitor, were the most potent FAP inhibitors in our dataset. In addition, by employing FAP DIANA in the quantification mode, we were able to determine FAP concentrations in human plasma samples. Together, our work expands the repertoire of FAP inhibitors, analyzes the potential interference of co-administered drugs with FAP-targeting strategies, and presents a sensitive and low-consumption ELISA alternative for FAP quantification with a detection limit of 50 pg/ml., Competing Interests: Declaration of competing interest DIANA technology is protected by patents US10718772 (B2) and US10718772 (B2) and ketoamide inhibitors are protected by patent US20230192647 (A1). The authors declare that they have no other conflicts of interest with the contents of this article., (Copyright © 2024 The Authors. Published by Elsevier Masson SAS.. All rights reserved.)

Lens epithelium-derived growth factor p75 (LEDGF/p75), member of the hepatoma-derived growth-factor-related protein (HRP) family, is a transcriptional co-activator and involved in several pathologies including HIV infection and malignancies such as MLL-rearranged leukemia. LEDGF/p75 acts by tethering proteins to the chromatin through its integrase binding domain. This chromatin interaction occurs between the PWWP domain of LEDGF/p75 and nucleosomes carrying a di- or trimethylation mark on histone H3 Lys36 (H3K36me2/3). Our aim is to rationally devise small molecule drugs capable of inhibiting such interaction. To bootstrap this development, we resorted to X-ray crystallography-based fragment screening (FBS-X). Given that the LEDGF PWWP domain crystals were not suitable for FBS-X, we employed crystals of the closely related PWWP domain of paralog HRP-2. As a result, as many as 68 diverse fragment hits were identified, providing a detailed sampling of the H3K36me2/3 pocket pharmacophore. Subsequent structure-guided fragment expansion in three directions yielded multiple compound series binding to the pocket, as verified through X-ray crystallography, nuclear magnetic resonance and differential scanning fluorimetry. Our best compounds have double-digit micromolar affinity and optimally sample the interactions available in the pocket, judging by the K d -based ligand efficiency exceeding 0.5 kcal/mol per non-hydrogen atom. Beyond π-stacking within the aromatic cage of the pocket and hydrogen bonding, the best compounds engage in a σ-hole interaction between a halogen atom and a conserved water buried deep in the pocket. Notably, the binding pocket in LEDGF PWWP is considerably smaller compared to the related PWWP1 domains of NSD2 and NSD3 which feature an additional subpocket and for which nanomolar affinity compounds have been developed recently. The absence of this subpocket in LEDGF PWWP limits the attainable affinity. Additionally, these structural differences in the H3K36me2/3 pocket across the PWWP domain family translate into a distinct selectivity of the compounds we developed. Our top-ranked compounds are interacting with both homologous LEDGF and HRP-2 PWWP domains, yet they showed no affinity for the NSD2 PWWP1 and BRPF2 PWWP domains which belong to other PWWP domain subfamilies. Nevertheless, our developed compound series provide a strong foundation for future drug discovery targeting the LEDGF PWWP domain as they can further be explored through combinatorial chemistry. Given that the affinity of H3K36me2/3 nucleosomes to LEDGF/p75 is driven by interactions within the pocket as well as with the DNA-binding residues, we suggest that future compound development should target the latter region as well. Beyond drug discovery, our compounds can be employed to devise tool compounds to investigate the mechanism of LEDGF/p75 in epigenetic regulation., Competing Interests: Declaration of competing interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (Crown Copyright © 2024. Published by Elsevier Masson SAS. All rights reserved.)

Metarrestin (ML246) is a first-in-class pyrrole-pyrimidine-derived small molecule that selectively targets the perinucleolar compartment (PNC). PNC is a distinct subnuclear structure predominantly found in solid tumor cells. The occurrence of PNC demonstrates a positive correlation with malignancy, serving as an indicator of tumor aggressiveness, progression, and metastasis. Various promising preclinical results have led to the clinical translation of metarrestin into a first-in-human trial. This review aims to summarize (i) the current understanding of the structure and function of PNC and its role in cancer progression and metastasis, (ii) key findings from studies examining the effect of metarrestin on various cancers across the translational spectrum, including in vitro, in vivo, and human clinical trial studies, and (iii) the pharmaceutical relevance of metarrestin as a promising anticancer candidate. Furthermore, our molecular docking and MD simulation studies show that metarrestin binds to eEF1A1 and eEF1A2 with a strong and stable affinity and inhibits eEF1A2 more efficiently compared to eEF1A1. The promising results from preclinical studies suggest that metarrestin has the potential to revolutionize the treatment of cancer, heralding a paradigm shift in its therapeutic management.

Kinetic stabilization of amyloidogenic immunoglobulin light chains (LCs) through small molecule binding may become the first treatment for the proteinopathy component of light chain amyloidosis (AL). Kinetic stabilizers selectively bind to the native state over the misfolding transition state, slowing denaturation. Prior λ full-length LC dimer (FL LC 2 ) kinetic stabilizers exhibited considerable plasma protein binding. We hypothesized that the coumarin "aromatic core" of the stabilizers was responsible for the undesirable plasma protein binding. Here, we describe structure-activity relationship (SAR) data initially focused on replacing the coumarin aromatic core. 2-pyridones proved suitable replacements. We subsequently optimized the "anchor substructure" in the context of 2-pyridones, resulting in potent λ FL LC 2 kinetic stabilizers exhibiting reduced plasma protein binding. The 3-methyl- or 3-ethyl-3-phenylpyrrolidine-2-pyridone scaffold stabilized multiple AL patient-derived λ FL LC 2 s in human plasma. This, coupled with X-ray crystallographic data, indicates that 3-alkyl-3-phenylpyrrolidine-2-pyridone-based stabilizers are promising candidates for treating the proteinopathy component of AL.

Cathepsin B (CTB) is overexpressed in several types of tumors, and precise evaluation of the CTB activity can offer a promising method for the early diagnosis of tumors. In this study, two CTB-activated positron emission tomography (PET) tracers, [ 68 Ga]NOTA-SFCVM and [ 68 Ga]NOTA-SFCVHEM , were developed for sensitive and specific detection of CTB. Both tracers undergo a click condensation between 2-cyano-6-aminobenzothiazole (CBT) and cysteine (Cys) to form a cyclization product, thereby enhancing and prolonging the PET signal in tumors. In vitro cellular experiments showed that the tracers could differentiate tumor cells with different expression levels of CTB. In vivo PET imaging further revealed that the tracers selectively accumulated in the CTB-positive tumors. Compared with [ 68 Ga]NOTA-SFCVM , [ 68 Ga]NOTA-SFCVHEM containing a morpholine group and a histidine-glutamate-histidine-glutamate-histidine-glutamate sequence exhibited faster catalytic efficiency toward CTB, higher tumor uptake, and reduced liver uptake. These findings suggest that [ 68 Ga]NOTA-SFCVHEM holds potential for clinical use in the early diagnosis of CTB-related tumors.

The discovery of small organic compounds for inducing stem cell differentiation is a time- and resource-intensive process. While data science could, in principle, streamline the discovery of these compounds, novel approaches are required due to the difficulty of acquiring training data from large numbers of example compounds. In this paper, we present the design of a new compound for inducing cardiomyocyte differentiation using simple regression models trained with a data set containing only 80 examples. We introduce decorated shape descriptors, an information-rich molecular feature representation that integrates both molecular shape and hydrophilicity information. These models demonstrate improved performance compared to ones using standard molecular descriptors based on shape alone. Model overtraining is diagnosed using a new type of sensitivity analysis. Our new compound is designed using a conservative molecular design strategy, and its effectiveness is confirmed through expression profiles of cardiomyocyte-related marker genes using real-time polymerase chain reaction experiments on human iPS cell lines. This work demonstrates a viable data-driven strategy for designing new compounds for stem cell differentiation protocols and will be useful in situations where training data is limited.

The concept of DNA-encoded libraries (DELs) enables the experimental screening of billions of compounds simultaneously, offering an unprecedented boost in the coverage of chemical space. In parallel, however, dramatically increased access to supercomputers and a number of ultrahigh throughput virtual screening (uHTVS) tools have made screening of billion-membered virtual libraries available. Here, we investigate whether current, brute-force, or AI-enabled uHTVS approaches might constitute a computational alternative to DEL screening. While it is tempting to look at uHTVS as a computational analogue of DEL screening, we found specific advantages and limitations of both methodologies that suggest them being complementary rather than competitive.

c-Jun N-terminal kinases (JNKs) including JNK1/2/3 are key members of mitogen-activated protein kinase family. Wherein JNK3 is specifically expressed in brain and emerges as therapeutic target, especially for neurodegenerative diseases. However, developing JNK3 selective inhibitors as chemical probes to investigate its therapeutic potential in diseases remains challenging. Here, we adopted the covalent strategy for identifying JNK3-selective covalent inhibitor JC16I, with high inhibitory activity against JNK3. Despite targeting a conserved cysteine in the vicinity of ATP pocket in JNK family, JC16I exerted a greater than 160-fold selectivity for JNK3 over JNK1/2. Importantly, even at low concentration, JC16I showed enhanced and long-lasting inhibition against cellular JNK3. In addition, its alkyne-containing probe JC-P1 could label JNK3 in SH-SY5Y cell lysate and living cells, with good proteome-wide selectivity. JC16I selectively suppressed the abnormal activation of JNK3 signaling and sufficiently exhibited neuroprotective effect in Parkinson's diseases (PD) models. Overall, our findings highlight the potential of developing isoform-selective and cell-active JNK3 inhibitors by covalent drug design strategy targeting a conserved cysteine. This work not only provides a valuable chemical probe for JNK3-targeted investigations in vitro and in vivo but also opens new avenues for the treatment of PD., (© 2024 Wiley-VCH GmbH.)

Protein-ligand binding affinity prediction is a crucial and challenging task in the field of drug discovery. However, traditional simulation-based computational approaches are often prohibitively time-consuming, limiting their practical utility. In this study, we introduce a novel deep learning method, CPIScore, which leverages the capabilities of Transformer and Graph Convolutional Networks (GCN) to enhance the prediction of protein-ligand binding affinity. CPIScore utilizes the Transformer architecture to capture comprehensive global contexts of protein and ligand sequences, while the GCN component effectively extracts local features from small molecular graphs. Our results demonstrate that CPIScore surpasses both traditional machine learning and other deep learning models in accuracy, achieving a Pearson's r of 0.74 on our test set. Furthermore, CPIScore has been validated across multiple targets, proving its ability to discern inhibitors from a diverse compound library with high enrichment rates. Notably, when applied to a generated focused library of compounds, CPIScore successfully identified six potent small-molecule inhibitors of ATR, which were tested experimentally and four small molecules exhibited inhibitory activity below ten nanomoles. These results highlight CPIScore's potential to significantly streamline and enhance the efficiency of drug discovery processes.

Proteins are dynamic macromolecules. Knowledge of a protein's thermally accessible conformations is critical to determining important transitions and designing therapeutics. Accessible conformations are highly constrained by a protein's structure such that concerted structural changes due to external perturbations likely track intrinsic conformational transitions. These transitions can be thought of as paths through a conformational landscape. Crystallographic drug fragment screens are high-throughput perturbation experiments, in which thousands of crystals of a drug target are soaked with small-molecule drug precursors (fragments) and examined for fragment binding, mapping potential drug binding sites on the target protein. Here, we describe an open-source Python package, COnformational LAndscape Visualization (COLAV), to infer conformational landscapes from such large-scale crystallographic perturbation studies. We apply COLAV to drug fragment screens of two medically important systems: protein tyrosine phosphatase 1B (PTP1B), which regulates insulin signaling, and the SARS CoV-2 Main Protease (MPro). With enough fragment-bound structures, we find that such drug screens enable detailed mapping of proteins' conformational landscapes.

A DNA-compatible three-component reaction is disclosed for the synthesis of on-DNA polysubstituted isoxazolidines that serve as privileged core scaffolds in numerous natural products and bioactive molecules. This one-pot approach involves the 1,3-dipolar cycloaddition of DNA-tagged styrenes with diazo compounds and nitrosoarenes in an aqueous solution of KOAc. The reaction demonstrates excellent functional group compatibility, providing a conventional protocol for the construction of a DNA-labeled isoxazolidine library.

The bioavailability of small-molecule drugs remains a critical challenge in pharmaceutical development, significantly impacting therapeutic efficacy and commercial viability. This review synthesizes recent advances in understanding and overcoming bioavailability limitations, focusing on key physicochemical and biological factors influencing drug absorption and distribution. We examine cutting-edge strategies for enhancing bioavailability, including innovative formulation approaches, rational structural modifications, and the application of artificial intelligence in drug design. The integration of nanotechnology, 3D printing, and stimuli-responsive delivery systems are highlighted as promising avenues for improving drug delivery. We discuss the importance of a holistic, multidisciplinary approach to bioavailability optimization, emphasizing early-stage consideration of ADME properties and the need for patient-centric design. This review also explores emerging technologies such as CRISPR-Cas9-mediated personalization and microbiome modulation for tailored bioavailability enhancement. Finally, we outline future research directions, including advanced predictive modeling, overcoming biological barriers, and addressing the challenges of emerging therapeutic modalities. By elucidating the complex interplay of factors affecting bioavailability, this review aims to guide future efforts in developing more effective and accessible small-molecule therapeutics.

Akt, a key regulator of cell survival, proliferation, and metabolism, has become a prominent target for treatment of cancer and inflammatory diseases. The journey of small-molecule Akt inhibitors from discovery to the clinic has faced numerous challenges, with a significant emphasis on optimization throughout the development process. Early discovery efforts identified various classes of inhibitors, including ATP-competitive and allosteric modulators. However, during preclinical and clinical development, several issues arose, including poor specificity, limited bioavailability, and toxicity. Optimization efforts have been central to overcoming these hurdles. Researchers focused on enhancing the selectivity of inhibitors to target Akt isoforms more precisely, reducing off-target effects, and improving pharmacokinetic properties to ensure better bioavailability and distribution. Structural modifications and the design of prodrugs have played a crucial role in refining the efficacy and safety profile of these inhibitors. Additionally, efforts have been made to optimize the therapeutic window, balancing effective dosing with minimal adverse effects. The review highlights how these optimization strategies have been key in advancing small-molecule Akt inhibitors toward clinical success and underscores the importance of continued refinement in their development., Competing Interests: Declaration of competing interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (Copyright © 2024 Elsevier Masson SAS. All rights reserved.)

Proteolysis-Targeting Chimeras (PROTACs) are a novel class of bifunctional small molecules that alter protein levels by targeted degradation. This innovative approach uses the ubiquitin-proteasome system to selectively eradicate disease-associated proteins, providing a novel therapeutic strategy for a wide spectrum of diseases. This review delineates detailed synthetic approaches involved in PROTAC building blocks, including the ligand and protein binding parts, linker attached structural components of PROTACs and the actual PROTAC molecules. Furthermore, the recent advancements in PROTAC-mediated degradation of specific oncogenic and other disease-associated proteins, such as those involved in neurodegenerative, antiviral, and autoimmune diseases, were also discussed. Additionally, we described the current landscape of PROTAC clinical trials and highlighted key studies that underscore the translational potential of this emerging therapeutic modality. These findings demonstrate the versatility of PROTACs in modulating the levels of key proteins involved in various severe diseases., Competing Interests: Declaration of competing interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (Copyright © 2024 The Authors. Published by Elsevier Masson SAS.. All rights reserved.)

Fragment-based drug discovery is a powerful approach in drug discovery, applicable to a wide range of targets. This method enables the discovery of potent compounds that can modulate target functions, starting from fragment compounds that bind weakly to the targets. While biochemical, biophysical, and cell-based assays are commonly used to identify fragments, 19 F-NMR spectroscopy has emerged as a powerful tool for exploring interactions between biomolecules and ligands. Because fluorine atoms are not naturally present in biological systems, 19 F-NMR serves as a sensitive method for fragment screening against diverse targets. Herein, we reviewed the applications of 19 F-NMR in fragment screening, highlighting its effectiveness in identifying fragments that bind weakly to various targets such as proteins and RNA. The accumulated evidence suggests that 19 F-NMR will continue to be a crucial tool in drug discovery.

Boron neutron capture therapy (BNCT) is a highly targeted, selective and effective technique to cure various types of cancers, with less harm to the healthy cells. In principle, BNCT treatment needs to distribute the 10 boron ( 10 B) atoms inside the tumor tissues, selectively and homogeneously, as well as to initiate a nuclear fission reaction by capturing sufficient neutrons which releases high linear energy particles to kill the tumor cells. In BNCT, it is crucial to have high quality boron agents with acceptable bio-selectivity, homogeneous distribution and deliver in required quantity, similar to chemotherapy and other radiotherapy for tumor treatment. Nevertheless, boron drugs currently used in clinical trials yet to meet the full requirements. On the other hand, BNCT processing has opened up the era of renaissance due to the advanced development of the high-quality neutron source and the global construction of new BNCT centers. Consequently, there is an urgent need to use boron agents that have increased biocapacity. Artificial intelligence (AI) tools such as molecular docking and molecular dynamic simulation technologies have been utilized to develop new medicines. In this work, the in silico assessments including bioinformatics assessments of BNCT related tumoral receptor proteins, computational assessments of optimized small molecules of boron agents, are employed to speed up the screening process for boron drugs. The outcomes will be applicable to pave the way for future BNCT that utilizes artificial intelligence. The in silico molecular docking and dynamic simulation results of the optimized small boron agents, such as 4-borono-l-phenylalanine (BPA) with optimized proteins like the L-type amino acid transporter 1 (LTA1, also known as SLC7A5) will be examined. The in silico assessments results will certainly be helpful to researchers in optimizing druggable boron agents for the BNCT application. The clinical status of the optimized proteins, which are highly relevant to cancers that may be treated with BNCT, has been assessed using bioinformatics technology and discussed accordingly. Furthermore, the evaluations of cytotoxicity (IC 50 ), boron uptake and tissue distribution of the optimized ligands 1 and 7 have been presented., Competing Interests: Declaration of competing interest All authors have read and agreed to the published version of the manuscript. The authors declare that the research was conducted without any commercial relationships that could lead to a potential conflict of interest., (Copyright © 2024 Elsevier Masson SAS. All rights reserved.)

A rapid and reliable evaluation of the aqueous solubility of small molecules is a hot topic for the scientific community and represents a field of particular interest in drug discovery. In fact, aqueous solubility significantly impacts various aspects that collectively influence a drug's overall pharmacokinetics, including absorption, distribution and metabolism. For this reason, in silico approaches that provide fast and cost-effective solubility predictions, can serve as invaluable tools in the early stages of drug development. Although additional molecular features should be considered, accurate solubility predictions can help medicinal chemists rationally planning the synthesis of compounds more likely to exhibit desirable pharmacokinetic properties and in selecting the most promising candidates for further biological testing (e.g., cellular assays) from an initial pool of hit compounds with detected preliminary bioactivity. In this context, we herein report the development and evaluation of WaSPred, our AI-based water solubility predictor for small molecules. WaSPred not only showed high reliability in water solubility predictions performed on structurally heterogeneous compounds, belonging to multiple external datasets, but also demonstrated superior performance compared to a set of other commonly used water solubility predictors, thus confirming its state-of the-art robustness and its usefulness as an in silico approach for water solubility evaluations.., Competing Interests: Declaration of competing interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (Copyright © 2024 Elsevier B.V. All rights reserved.)

Photodynamic therapy (PDT) employing two-photon (TP) excitation is increasingly recognized to induce cell damage selectively in targeted areas, underscoring the importance of developing TP photosensitizers (TP-PSs). In this study, we developed BSe-B, a novel PS that combines a selenium containing dye with biotin, a cancer-selective ligand, and is optimized for TP excitation. BSe-B demonstrated enhanced cancer selectivity, efficient generation of type-I based reactive oxygen species (ROS), low dark toxicity, and excellent cell-staining capability. Evaluation across diverse cell lines (HeLa, A549, OVCAR-3, WI-38, and L-929) demonstrated that BSe-B differentiated and targeted cancer cells while sparing normal cells. BSe-B displayed excellent in vivo biocompatibility. In cancer models such as three-dimensional spheroids and actual colon cancer tissues, BSe-B selectively induced ROS production and cell death under TP irradiation, demonstrating precise spatial control. These findings highlight the potential of BSe-B for imaging-guided PDT and its capability for micro treatment within tissues. Thus, BSe-B demonstrates robust TP-PDT capabilities, making it a promising dual-purpose tool for cancer diagnosis and treatment.

Although small molecule superposition is a standard technique in drug discovery, a rigorous performance assessment of the corresponding methods is currently challenging. Datasets in this field are sparse, small, tailored to specific applications, unavailable, or outdated. The newly developed LOBSTER set described herein offers a publicly available and method-independent dataset for benchmarking and method optimization. LOBSTER stands for "Ligand Overlays from Binding SiTe Ensemble Representatives". All ligands were derived from the PDB in a fully automated workflow, including a ligand efficiency filter. So-called ligand ensembles were assembled by aligning identical binding sites. Thus, the ligands within the ensembles are superimposed according to their experimentally determined binding orientation and conformation. Overall, 671 representative ligand ensembles comprise 3583 ligands from 3521 proteins. Altogether, 72,734 ligand pairs based on the ensembles were grouped into ten distinct subsets based on their volume overlap, for the benefit of introducing different degrees of difficulty for evaluating superposition methods. Statistics on the physicochemical properties of the compounds indicate that the dataset represents drug-like compounds. Consensus Diversity Plots show predominantly high Bemis-Murcko scaffold diversity and low median MACCS fingerprint similarity for each ensemble. An analysis of the underlying protein classes further demonstrates the heterogeneity within our dataset. The LOBSTER set offers a variety of applications like benchmarking multiple as well as pairwise alignments, generating training and test sets, for example based on time splits, or empirical software performance evaluation studies. The LOBSTER set is publicly available at https://doi.org/10.5281/zenodo.12658320 , representing a stable and versioned data resource. The Python scripts are available at https://github.com/rareylab/LOBSTER , open-source, and allow for updating or recreating superposition sets with different data sources., Competing Interests: Declarations. Conflict of interest: Christian Lemmen and Matthias Rarey are shareholders of BioSolveIT GmbH, a company licensing the superposition software FlexS commercially. Ethical approval: Not applicable., (© 2024. The Author(s).)

Rapid equilibrium dialysis (RED) is predominantly used for the characterization of drug absorption, distribution, metabolism, and excretion (ADME) properties in plasma and biological fluids. We describe herein improvements in the use of RED in conjunction with mass spectrometry (RED-MS) to enable robust binding affinity measurements of small molecules for recombinant proteins and complexes from a single dialysis data set. The affinities calculated from RED-MS correlated well with measurements by both surface plasmon resonance (SPR) and affinity selection mass spectrometry (AS-MS). The method was particularly useful for quantifying the binding of small molecules to large protein complexes that were not amendable by common biophysical characterization techniques. Compound pooling and integration with automated liquid handling increased assay throughput and enabled the analysis of hundreds of measurements per week. RED-MS offers a viable option for measuring compound binding in solution and may facilitate small molecule affinity optimization toward difficult-to-drug protein complexes.

Sulfation is one of the most important modifications that occur to a wide range of bioactive small molecules including polysaccharides, proteins, flavonoids, and steroids. In turn, these sulfated molecules have significant biological and pharmacological roles in diverse processes including cell signalling, modulation of immune and inflammation response, anti-coagulation, anti-atherosclerosis, and anti-adhesive properties. This Essay summarises the most encountered chemical sulfation methods of small molecules. Sulfation reactions using sulfur trioxide amine/amide complexes are the most used method for alcohol and phenol groups in carbohydrates, steroids, proteins, and related scaffolds. Despite the effectiveness of these methods, they suffer from issues including multiple-purification steps, toxicity issues (e.g., pyridine contamination), purification challenges, stoichiometric excess of reagents which leads to an increase in reaction cost, and intrinsic stability issues of both the reagent and product. Recent advances including SuFEx, the in situ reagent approach, and TBSAB show the widespread appeal of novel sulfating approaches that will enable a larger exploration of the field in the years to come by simplifying the purification and isolation process to access bespoke sulfated small molecules., (© 2024 The Author(s).)

Resistance to anti-microbial agents is a world-wide health threat. Thus, there is an urgent need for new treatments. An alternative approach to disarm pathogens consists in developing drugs targeting epigenetic modifiers. Bacterial pathogens can manipulate epigenetic regulatory systems of the host to bypass defences to proliferate and survive. One example is Legionella pneumophila, a Gram-negative intracellular pathogen that targets host chromatin with a specific, secreted bacterial SET-domain methyltransferase named RomA. This histone methyltransferase specifically methylates H3 K14 during infection and is responsible for changing the host epigenetic landscape upon L. pneumophila infection. To inhibit RomA activity during infection, we developed a reliable high-content imaging screening assay, which we used to screen an in-house chemical library developed to inhibit DNA and histone methyltransferases. This assay was optimised using monocytic leukemic THP-1 cells differentiated into macrophages infected with L. pneumophila in a 96- or 384-well plate format using the Opera Phenix (Perkin Elmer) confocal microscope, combined with Columbus software for automated image acquisition and analysis. H3 K14 methylation was followed in infected, single cells and cytotoxicity was assessed in parallel. A first pilot screening of 477 compounds identified a potential starting point for inhibitors of H3 K14 methylation., (© 2024 Wiley-VCH GmbH.)

Cellular death evasion is a defining characteristic of human malignancies and a significant contributor to therapeutic inefficacy. As a result of oncogenic inhibition of cell death mechanisms, established therapeutic regimens seems to be ineffective. Mitochondria serve as the cellular powerhouses, but they also function as repositories of self-destructive weaponry. Changes in the structure and activities of mitochondria have been consistently documented in cancer cells. In recent years, there has been an increasing focus on using mitochondria as a targeted approach for treating cancer. Considerable attention has been devoted to the development of delivery systems that selectively aim to deliver small molecules called "mitocans" to mitochondria, with the ultimate goal of modulating the physiology of cancer cells. This review summarizes the rationale and mechanism of mitochondrial targeting with small molecules in the treatment of cancer, and their impact on the mitochondria. This paper provides a concise overview of the reasoning and mechanism behind directing treatment towards mitochondria in cancer therapy, with a particular focus on targeting using small molecules. This review also examines diverse small molecule types within each category as potential therapeutic agents for cancer., Competing Interests: Declaration of competing interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (Copyright © 2024 Elsevier Inc. All rights reserved.)

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 IC 50 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., Competing Interests: Declaration of competing interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (Copyright © 2024 Elsevier B.V. All rights reserved.)

Immune-inflammatory diseases are a class of conditions with high prevalence that severely impact the quality of life. Current treatment strategies include immunosuppressants, glucocorticoids, and monoclonal antibodies. However, these approaches have certain limitations, such as poor membrane permeability, immunogenicity, and the requirement for injection in large molecule drugs. Small molecule compounds, on the other hand, suffer from issues like poor selectivity, inability to inhibit non-enzymatic functions, and biological compensation. These factors constrain the effectiveness of current therapeutic strategies in immune-inflammatory diseases. As a novel small molecule drug development technology, proteolysis-targeting chimeras (PROTACs) regulate protein levels by inducing interactions between target proteins and E3 ubiquitin ligases, leading to the selective degradation of target proteins. This technology has already shown promising therapeutic effects in the treatment of immune-inflammatory diseases. This review aims to comprehensively summarize the application of PROTAC technology in the field of immune inflammation and provide insights into its potential in treating immune-inflammatory diseases., Competing Interests: Declaration of competing interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (Copyright © 2024 Elsevier Ltd. All rights reserved.)

Dry DMSO can rapidly pull water vapor out of the air due to its hygroscopic nature. This is a well-documented problem within drug discovery, particularly within high-throughput screening (HTS). This hydration is caused by atmospheric moisture being absorbed each time a compound library is used. This effect becomes increasingly pronounced when a compound library is used routinely. The result of this hydration is a change to both the total volume of solution and the concentration of sample still in solution. This can result in a large amount of variability in the measured biological activity of a sample depending on the library usage. In this paper, we show the detrimental effects the hydration of sample libraries has on the reproducibility of biological data and present a novel way to remove it from HTS library plates. Our approach involves creating a DMSO-rich environment, created by placing anhydrous DMSO in compound storage pods purged with nitrogen, and incubating library plates in this environment for up to 3 days. Quantification via evaporative light scattering detection (ELSD) showed that removing water greatly increased the molarity of solutions, with a greater effect being seen for compounds with poor solubility. We also demonstrated how this approach can restore the inhibitory activity of stock solutions of compounds (pIC 50 ) of samples containing ∼30 % water from >30 µM to sub-micromolar after moisture removal. This method improves the reliability of tested compounds in HTS by potentially saving pharmaceutical companies hundreds of thousands of dollars in screening campaigns and increasing the quality of data., Competing Interests: Declaration of competing interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (Copyright © 2024. Published by Elsevier Inc.)

The treatment of bladder cancer is limited by low drug efficacy and drug resistance. Hence, this study aimed to screen and identify potential drug precursors and investigate their mechanism of action. A set of camptothecin derivatives showing high anti-tumor potential was selected from early-stage research or literature and synthesized to construct a compound library. A total of 135 compounds were screened in T24 and J82 cells, revealing that FL118 significantly inhibited the proliferation of GC (gemcitabine + cisplatin)-sensitive/insensitive cells. FL118 exhibited excellent penetration and killing ability in organoids and three GC-insensitive patient-derived xenografts. Chemical proteomic and docking calculations were employed to identify binding proteins, indicating that FL118 can bind into H2A.X and its entwined DNA. The results of Cellular thermal shift assay and surface plasmon resonance (K d  = 3.77E-6) support the above findings. Fluorescence localization revealed widespread binding of FL118 within the cell nucleus. Furthermore, WB showed that FL118 increased cellular DNA damage, resulting in significant cell cycle inhibition. The binding of FL118 to H2A.X hindered the damage repair process, leading to apoptosis. Controllable adverse reactions were observed in mice treated with FL118. In conclusion, FL118 may be a superior anti-bladder cancer compound that acts as a molecular glue binding to both H2A.X and DNA. The resistance mediated by the DNA damage repair to DNA damage caused by GC regimen can be reversed by FL118. This distinct mechanism of FL118 has the potential to complement existing mainstream treatment approaches for bladder cancer., Competing Interests: Declaration of competing interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (Copyright © 2024 Elsevier Inc. All rights reserved.)

An approach is described for high-throughput quality assessment of drug candidate libraries using high-resolution acoustic ejection mass spectrometry (AEMS). Sample introduction from 1536-well plates is demonstrated for this application using 2.5 nL acoustically dispensed sample droplets into an Open Port Interface (OPI) with pneumatically assisted electrospray ionization at a rate of one second per sample. Both positive and negative ionization are shown to be essential to extend the compound coverage of this protease inhibitor-focused library. Specialized software for efficiently interpreting this data in 1536-well format is presented. A new high-throughput method for quantifying the concentration of the components (HTQuant) is proposed that neither requires adding an internal standard to each well nor further encumbers the high-throughput workflow. This approach for quantitation requires highly reproducible peak areas, which is shown to be consistent within 4.4 % CV for a 1536-well plate analysis. An approach for troubleshooting the workflow based on the background ion current signal is also presented. The AEMS data is compared to the industry standard LC/PDA/ELSD/MS approach and shows similar coverage but at 180-fold greater throughput. Despite the same ionization process, both methods confirmed the presence of a small percentage of compounds in wells that the other did not. The data for this relatively small, focused library is compared to a larger, more chemically diverse library to indicate that this approach can be more generally applied beyond this single case study. This capability is particularly timely considering the growing implementation of artificial intelligence strategies that require the input of large amounts of high-quality data to formulate predictions relevant to the drug discovery process. The molecular structures of the 872-compound library analyzed here are included to begin the process of correlating molecular structures with ionization efficiency and other parameters as an initial step in this direction., Competing Interests: Declaration of competing interest The authors declare no competing interests., (Published by Elsevier Inc.)

Molecular design of small-molecule inhibitors targeting programmed cell death-1 (PD-1)/programmed cell death ligand-1 (PD-L1) pathway has been recognized as an active research area by the clinical success of cancer immunotherapy. In recent years, using machine learning (ML) methods to accelerate drug design have been confirmed. However, the black box character of ML methods makes model interpretation and ligands optimization obscured. Herein, five explainable ML models were constructed by integrating five ML models with the SHAP method, where these ML models were pretrained with >4000 molecules and their R 2 ranged from 0.835 to 0.86 on test set. Subsequently, the explainable ML models were employed to identify the relationship between fragments and bio-activity of a small molecule inhibitor BMS-1166, leading to the modification of BMS-1166 into 60 novel compounds. After consensus docking and ADMET test, 3 small molecules (C27, C52 and C54) with better docking scores and lower toxicity than BMS-1166 were screened out further. Finally, the improved binding affinity of C27, C52 to the PD-L1 dimer was validated by the MD simulation. Overall, this work proposed an efficient protocol on the basis of explainable ML models for designing small-molecule inhibitors targeting PD-1/PD-L1 pathway in a rational way., Competing Interests: Declaration of competing interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (Copyright © 2024 Elsevier B.V. All rights reserved.)

Given the increasing effectiveness of immune-based therapies, management of their associated toxicities is of utmost importance. Cytokine release syndrome (CRS), characterized by elevated levels of cytokine, poses a significant challenge following the administration of antibodies and CAR-T cell therapies. CRS also contributes to multiple organ dysfunction in severe viral infections, notably in COVID-19. Given the pivotal role of IL-6 cytokine in initiating CRS, it has been considered a most potential therapeutic target to mitigate hyperactivated immune responses. While monoclonal antibodies of IL-6 show promise in mitigating cytokine storm, concerns about immunotoxicity persist, and small molecule IL-6 antagonists remain unavailable. The present study employed sophisticated computational techniques to identify potential hit compounds as IL-6 inhibitors, with the aim of inhibiting IL-6/IL-6R protein-protein interactions. Through ligand-based pharmacophore mapping and shape similarity in combination with docking-based screening, we identified nine hit compounds with diverse chemical scaffolds as potential binders of IL-6. Further, the MD simulation of 300 ns of five virtual hits in a complex with IL-6 was employed to study the dynamic behavior. To provide a more precise prediction, binding free energy was also estimated. The identified compounds persistently interacted with the residues lining the binding site of the IL-6 protein. These compounds displayed low binding energy during MMPBSA calculations, substantiating their strong association with IL-6. This study suggests promising scaffolds as potential inhibitors of IL-6/IL-6R protein-protein interactions and provides direction for lead optimization., Competing Interests: Declarations. Competing interests: The authors report there are no competing interests to declare., (© 2024. The Author(s), under exclusive licence to Springer Nature Switzerland AG.)

The advent of high-performance virtual screening techniques nowadays allows drug designers to explore ultra-large sets of candidate compounds in search of molecules predicted to have desired properties. However, the success of such an endeavor heavily relies on the pertinence (drug-likeness and, foremost, chemical feasibility) of these candidates, or otherwise, virtual screening will return valueless "hits", by the garbage in/garbage out principle. The huge popularity of the judiciously enumerated Enamine REAL Space is clear proof of the strength of this Big Data trend in drug discovery. Here we describe a new dataset of make-on-demand compounds called the Freedom space. It follows the principles of Enamine REAL Space and contains highly feasible molecules (synthesis success rate over 75 percent). However, the scaffold and chemography analysis revealed significant differences to both the REAL and biologically annotated compounds from the ChEMBL database. The Freedom Space is a significant extension of the REAL Space and can be utilized for a more comprehensive exploration of the synthetically feasible chemical space in hit finding and hit-to-lead campaigns., (© 2024 Wiley-VCH GmbH.)

Pancreatic cancer, with a 5-year survival rate below 10 %, is one of the deadliest malignancies. The TGF-ß pathway plays a crucial role in this disease, making it a key target for therapeutic intervention. Clinical trials targeting TGF-β have faced challenges of toxicity and limited efficacy, highlighting the need for more potent small molecule inhibitors. We selected TGFßR1 as the drug target to inhibit TGF-ß signaling in pancreatic cancer. A multi-faceted approach was employed, commencing with AI-driven screening techniques to rapidly identify potential TGFßR1 inhibitors from vast compound libraries, including the ZINC and ChEMBL databases. AI-screened compounds were further validated through structure-based high-throughput virtual screening (HTVS) to evaluate their binding affinity to TGFßR1. In addition to this, a dedicated library of anticancer compounds (65,000 compounds) and protein kinase inhibitors (36,324 compounds) were also used for HTVS. Subsequently, pharmacokinetic profiling narrowed the selection to 40 hit compounds. Five hit compounds were chosen based on binding affinity, non-bonded interactions, stereochemistry, and pharmacokinetic profiles for molecular dynamics (MD) simulations. Trajectory analysis showed that residues HIS283, ASP351, LYS232, SER280, ILE211, and LYS213 within TGFßR1's active site are crucial for ligand binding through hydrogen bonds and hydrophobic interactions. Principal component analysis (PCA) and Dynamic cross-correlation matrix (DCCM) analysis were used to evaluate the receptor's dynamic response to the hit compounds. The simulation data revealed that compounds 1, 2, 3, 4, and 5 formed stable complexes with TGFßR1. Notably, post-MDS MM-GBSA analysis showed that compounds 4 and 5 exhibited exceptionally strong binding energies of -81.0 kcal/mol and -85.5 kcal/mol, respectively. The comprehensive computational analysis confirms compounds 4 and 5 as promising TGFßR1 hits with potential therapeutic applications in development of new treatments for pancreatic cancer., Competing Interests: Declaration of Competing Interest No potential conflict of interest was reported by the author(s)., (Copyright © 2024 Elsevier Ltd. All rights reserved.)