A Robust CETSA Data Analysis Automation Workflow for Routine Screening.

The Cellular Thermal Shift Assay (CETSA) enables the study of protein-ligand interactions in a cellular context. It provides valuable information on the binding affinity and specificity of both small and large molecule ligands in a relevant physiological context, hence forming a unique tool in drug discovery. Though high-throughput lab protocols exist for scaling up CETSA, subsequent data analysis and quality control remain laborious and limit experimental throughput. Here, we introduce a scalable and robust data analysis workflow which allows integration of CETSA into routine high throughput screening (HT-CETSA). This new workflow automates data analysis and incorporates quality control (QC), including outlier detection, sample and plate QC, and result triage. We describe the workflow and show its robustness against typical experimental artifacts, show scaling effects, and discuss the impact of data analysis automation by eliminating manual data processing steps.

Current strategies to address data scarcity in artificial intelligence-based drug discovery: A comprehensive review.

Artificial intelligence (AI) has played a vital role in computer-aided drug design (CADD). This development has been further accelerated with the increasing use of machine learning (ML), mainly deep learning (DL), and computing hardware and software advancements. As a result, initial doubts about the application of AI in drug discovery have been dispelled, leading to significant benefits in medicinal chemistry. At the same time, it is crucial to recognize that AI is still in its infancy and faces a few limitations that need to be addressed to harness its full potential in drug discovery. Some notable limitations are insufficient, unlabeled, and non-uniform data, the resemblance of some AI-generated molecules with existing molecules, unavailability of inadequate benchmarks, intellectual property rights (IPRs) related hurdles in data sharing, poor understanding of biology, focus on proxy data and ligands, lack of holistic methods to represent input (molecular structures) to prevent pre-processing of input molecules (feature engineering), etc. The major component in AI infrastructure is input data, as most of the successes of AI-driven efforts to improve drug discovery depend on the quality and quantity of data, used to train and test AI algorithms, besides a few other factors. Additionally, data-gulping DL approaches, without sufficient data, may collapse to live up to their promise. Current literature suggests a few methods, to certain extent, effectively handle low data for better output from the AI models in the context of drug discovery. These are transferring learning (TL), active learning (AL), single or one-shot learning (OSL), multi-task learning (MTL), data augmentation (DA), data synthesis (DS), etc. One different method, which enables sharing of proprietary data on a common platform (without compromising data privacy) to train ML model, is federated learning (FL). In this review, we compare and discuss these methods, their recent applications, and limitations while modeling small molecule data to get the improved output of AI methods in drug discovery. Article also sums up some other novel methods to handle inadequate data.

Synthesis and activity of benzimidazole N-Acylhydrazones against Trypanosoma cruzi, Leishmania amazonensis and Leishmania infantum.

In this study, we present the design, synthesis, and cytotoxic evaluation of a series of benzimidazole N-acylhydrazones against strains of T. cruzi (Y and Tulahuen) and Leishmania species (L. amazonensis and L. infantum). Compound (E)-N'-((5-Nitrofuran-2-yl)methylene)-1H-benzo[d]imidazole-2-carbohydrazide demonstrated significant activity against both trypomastigote and amastigote forms (Tulahuen strain), with an IC50/120 h of 0.033 µM and a selectivity index (SI) of 7680. This represents a potency 46 times greater than that of benznidazole (IC50/120 h = 1.520 µM, SI = 1390). Another compound (E)-N'-(2-Hydroxybenzylidene)-1H-benzo[d]imidazole-2-carbohydrazide showed promising activity against both trypomastigote and amastigote forms (Tulahuen strain), with an IC50/120 h of 3.600 µM and an SI of 14.70. However, its efficacy against L. infantum and L. amazonensis was comparatively lower. These findings provide valuable insights for the development of more effective treatments against Trypanosoma cruzi.

An update on the novel methods for the discovery of antiseizure and antiepileptogenic medications: where are we in 2024?

INTRODUCTION: Despite the availability of around 30 antiseizure medications, 1/3 of patients with epilepsy fail to become seizure-free upon pharmacological treatment. Available medications provide adequate symptomatic control in two-thirds of patients, but disease-modifying drugs are still scarce. Recently, though, new paradigms have been explored. AREAS COVERED: Three areas are reviewed in which a high degree of innovation in the search for novel antiseizure and antiepileptogenic medications has been implemented: development of novel screening approaches, search for novel therapeutic targets, and adoption of new drug discovery paradigms aligned with a systems pharmacology perspective. EXPERT OPINION: In the past, worldwide leaders in epilepsy have reiteratively stated that the lack of progress in the field may be explained by the recurrent use of the same molecular targets and screening procedures to identify novel medications. This landscape has changed recently, as reflected by the new Epilepsy Therapy Screening Program and the introduction of many in vitro and in vivo models that could possibly improve our chances of identifying first-in-class medications that may control drug-resistant epilepsy or modify the course of disease. Other milestones include the study of new molecular targets for disease-modifying drugs and exploration of a systems pharmacology perspective to design new drugs.

Tackling neurodegeneration in vitro with omics: a path towards new targets and drugs.

Drug discovery is a generally inefficient and capital-intensive process. For neurodegenerative diseases (NDDs), the development of novel therapeutics is particularly urgent considering the long list of late-stage drug candidate failures. Although our knowledge on the pathogenic mechanisms driving neurodegeneration is growing, additional efforts are required to achieve a better and ultimately complete understanding of the pathophysiological underpinnings of NDDs. Beyond the etiology of NDDs being heterogeneous and multifactorial, this process is further complicated by the fact that current experimental models only partially recapitulate the major phenotypes observed in humans. In such a scenario, multi-omic approaches have the potential to accelerate the identification of new or repurposed drugs against a multitude of the underlying mechanisms driving NDDs. One major advantage for the implementation of multi-omic approaches in the drug discovery process is that these overarching tools are able to disentangle disease states and model perturbations through the comprehensive characterization of distinct molecular layers (i.e., genome, transcriptome, proteome) up to a single-cell resolution. Because of recent advances increasing their affordability and scalability, the use of omics technologies to drive drug discovery is nascent, but rapidly expanding in the neuroscience field. Combined with increasingly advanced in vitro models, which particularly benefited from the introduction of human iPSCs, multi-omics are shaping a new paradigm in drug discovery for NDDs, from disease characterization to therapeutics prediction and experimental screening. In this review, we discuss examples, main advantages and open challenges in the use of multi-omic approaches for the in vitro discovery of targets and therapies against NDDs.

Identification of potential PIM-2 inhibitors via ligand-based generative models, molecular docking and molecular dynamics simulations.

Proviral Integrations of Moloney-2 (PIM-2) kinase is a promising target for various cancers and other diseases, and its inhibitors hold potential for treating related diseases. However, there is currently no clinically available PIM-2 inhibitor. In this study, we constructed a generative model for de novo PIM-2 inhibitor design based on artificial intelligence, performed molecular docking and molecular dynamics (MD) simulations to develop an efficient PIM-2 inhibitor generative model and discover potential PIM-2 inhibitors. First, we designed a generative model based on a Bi-directional Long Short-Term Memory (BiLSTM) framework combined with a transfer learning strategy and generated a new PIM-2 small molecule library using existing active drug databases. The generated compound library was then virtually screened by molecular docking and scaffold similarity comparison, identifying 10 initial hit compounds with better performance. Next, using the inhibitor in the crystal structure as a positive control, we performed two rounds of MD simulations, with lengths of 100 ns and 500 ns, respectively, to study the dynamic stability of the protein-ligand systems of the 10 compounds with PIM-2. Analyzed the interactions with key hinge region residues, binding free energies, and changes in the ATP pocket size. The generative model demonstrates good molecular generation capability and can generate efficient novel molecules with similar physicochemical properties as active PIM-2 drugs. Among the 10 initially selected hit compounds, 5 compounds C3 (- 29.69 kcal/mol), C4 (- 33.31 kcal/mol), C5 (- 28.59 kcal/mol), C8 (- 34.68 kcal/mol), and C9 (- 25.88 kcal/mol) have higher binding energies with PIM-2 than the positive drug 3YR (- 26.18 kcal/mol). The MD simulation results are consistent with the docking analysis, these compounds have lower and more stable RMSD values for the complex systems with the reported positive drug 3YR and PIM-2 complex system. They can form long-term stable interactions with active site and the hinge region of PIM-2, which suggests these compounds are likely to have potent inhibitory effects on PIM-2. This study provides an efficient generative model for PIM-2 inhibitor research and discovers 5 potential novel PIM-2 inhibitors.

Preclinical antidiabetic and antioxidant effects of Erythrophleum africanum (benth.) harms in streptozotocin-induced diabetic nephropathy.

OBJECTIVES: This study investigated the antidiabetic effects of the methanolic extract of E. africanum (MEEA) stem bark on streptozotocin (STZ)-induced diabetic nephropathy (DN) in Wistar rats. METHODS: The in vitro enzyme (α-amylase) inhibitory activity of MEEA was measured using a standard procedure. Diabetic rats with fasting blood glucose above 250 mg/dL were considered diabetic and were divided into the following groups: control (distilled water-treated), diabetic-control, diabetic metformin (100 mg/kg), diabetes + MEEA (150 mg/kg), and diabetes + MEEA (300 mg/kg) via oral gavage once daily for 14 days. At the end of the experimental period, kidney tissues were collected for biochemical and histological analyses. Kidney apoptosis and marker gene expression were measured by real-time quantitative PCR. RESULTS: MEEA exhibited α-amylase inhibitory effects. MEEA significantly (p<0.05) reduced the STZ-induced increases in blood glucose, serum urea, serum creatinine, uric acid, alanine aminotransferase, alkaline phosphatase, and malondialdehyde and increased the STZ-induced decreases in superoxide dismutase, catalase, and reduced glutathione. In addition, MEEA protects against DN by significantly downregulating the mRNA expression of cyclic adenosine monophosphate (cAMP), protein kinase A (PKA), cAMP-response binding protein (CREB), and cFOS and upregulating B-cell lymphoma 2 (Bcl-2), suggesting that the nephroprotective ability of MEEA is due to the modulation of the cAMP/PKA/CREB/cFOS signaling pathway. Furthermore, MEEA treatment protected against histopathological alterations observed in diabetic rats. CONCLUSIONS: The data from this study suggest that MEEA modulates glucose homeostasis and inhibits redox imbalance in DN rats.

SB-Net: Synergizing CNN and LSTM networks for uncovering retrosynthetic pathways in organic synthesis.

Retrosynthesis is vital in synthesizing target products, guiding reaction pathway design crucial for drug and material discovery. Current models often neglect multi-scale feature extraction, limiting efficacy in leveraging molecular descriptors. Our proposed SB-Net model, a deep-learning architecture tailored for retrosynthesis prediction, addresses this gap. SB-Net combines CNN and Bi-LSTM architectures, excelling in capturing multi-scale molecular features. It integrates parallel branches for processing one-hot encoded descriptors and ECFP, merging through dense layers. Experimental results demonstrate SB-Net's superiority, achieving 73.6 % top-1 and 94.6 % top-10 accuracy on USPTO-50k data. Versatility is validated on MetaNetX, with rates of 52.8 % top-1, 74.3 % top-3, 79.8 % top-5, and 83.5 % top-10. SB-Net's success in bioretrosynthesis prediction tasks indicates its efficacy. This research advances computational chemistry, offering a robust deep-learning model for retrosynthesis prediction. With implications for drug discovery and synthesis planning, SB-Net promises innovative and efficient pathways.

International Union of Basic and Clinical Pharmacology. CXV: The Class F of G Protein-Coupled Receptors.

The class F of G protein-coupled receptors (GPCRs) consists of ten Frizzleds (FZD1-10) and Smoothened (SMO). FZDs bind and are activated by secreted lipoglycoproteins of the Wingless/Int-1 (WNT) family and SMO is indirectly activated by the Hedgehog (Hh) family of morphogens acting on the transmembrane protein Patched (PTCH). The advance of our understanding of FZDs and SMO as dynamic transmembrane receptors and molecular machines, which emerged during the past 14 years since the first class F GPCR IUPHAR nomenclature report, justifies an update. This article focuses on the advances in molecular pharmacology and structural biology providing new mechanistic insight into ligand recognition, receptor activation mechanisms, signal initiation and signal specification. Furthermore, class F GPCRs continue to develop as drug targets, and novel technologies and tools such as genetically encoded biosensors and CRISP/Cas9 edited cell systems have contributed to refined functional analysis of these receptors. Also, advances in crystal structure analysis and cryogenic electron microscopy contribute to a rapid development of our knowledge about structure-function relationships providing a great starting point for drug development. Despite the progress questions and challenges remain to fully understand the complexity of the WNT/FZD and Hh/SMO signaling systems. Significance Statement The recent years of research have brought about substantial functional and structural insight into mechanisms of activation of Frizzleds and Smoothened. While the advance furthers our mechanistic understanding of ligand recognition, receptor activation, signal specification and initiation, broader opportunities emerge that allow targeting class F GPCRs for therapy and regenerative medicine employing both biologics and small molecule compounds.

Integrating bacterial molecular genetics with chemical biology for renewed antibacterial drug discovery.

The application of dyes to understanding the aetiology of infection inspired antimicrobial chemotherapy and the first wave of antibacterial drugs. The second wave of antibacterial drug discovery was driven by rapid discovery of natural products, now making up 69% of current antibacterial drugs. But now with the most prevalent natural products already discovered, ∼107 new soil-dwelling bacterial species must be screened to discover one new class of natural product. Therefore, instead of a third wave of antibacterial drug discovery, there is now a discovery bottleneck. Unlike natural products which are curated by billions of years of microbial antagonism, the vast synthetic chemical space still requires artificial curation through the therapeutics science of antibacterial drugs - a systematic understanding of how small molecules interact with bacterial physiology, effect desired phenotypes, and benefit the host. Bacterial molecular genetics can elucidate pathogen biology relevant to therapeutics development, but it can also be applied directly to understanding mechanisms and liabilities of new chemical agents with new mechanisms of action. Therefore, the next phase of antibacterial drug discovery could be enabled by integrating chemical expertise with systematic dissection of bacterial infection biology. Facing the ambitious endeavour to find new molecules from nature or new-to-nature which cure bacterial infections, the capabilities furnished by modern chemical biology and molecular genetics can be applied to prospecting for chemical modulators of new targets which circumvent prevalent resistance mechanisms.

CNS Drug Discovery in Academia: Where Basic Research Meets Innovation.

The involvement of academic research in drug discovery is consistently growing. However, academic projects seldom advance to clinical trials. Here, we assess the landscape of drug discovery within the National Centre of Competence in Research (NCCR) TransCure launched by the Swiss National Science Foundation to foster basic research and early-stage drug discovery on membrane transporters. This included transporters in central nervous system (CNS) disorders, which represent a huge unmet medical need. While idea championship, sustainable funding, collaborations between disciplines at the interface of academia and industry are important for translational research, Popperian falsifiability, strong intellectual property and a motivated startup team are key elements for innovation. This is exemplified by the NCCR TransCure spin-off company Synendos Therapeutics, a clinical stage biotech company developing the first selective endocannabinoid reuptake inhibitors (SERIs) as novel treatment for neuropsychiatric disorders. We provide a perspective on the challenges related to entering an uncharted druggable space and bridging the often mentioned "valley of death". The high attrition rate of drug discovery projects in the CNS field within academia is often due to the lack of meaningful animal models that can provide pharmacological proof-of-concept for potentially disruptive technologies at the earliest stages, and the absence of solid intellectual property.

New approaches to the control of chronic inflammatory diseases with a focus on the endolysosomal system of immune cells.

Chronic inflammation is implicated in many types of diseases, including cardiovascular, neurodegenerative, metabolic, and immune disorders. The search for therapeutic targets to control chronic inflammation often involves narrowing down the various molecules associated with pathology that have been discovered by various omics analyses. Herein, a different approach to identify therapeutic targets against chronic inflammation is proposed and one such target is discussed as an example. In chronically inflamed tissues, a large number of cells receive diverse proinflammatory signals, the intracellular signals are intricately integrated, and complicated intercellular interactions are orchestrated. This review focuses on effectively blocking this chaotic inflammatory signaling network via the endolysosomal system, which acts as a cellular signaling hub. In endolysosomes, the inflammatory signals mediated by pathogen sensors, such as Toll-like receptors, and the signals from nutrient and metabolic pathways are integrally regulated. Disruption of endolysosome signaling results in a strong anti-inflammatory effect by disrupting various signaling pathways, including pathogen sensor-mediated signals, in multiple immune cells. The endolysosome-resident amino acid transporter, solute carrier family 15 member 4 (SLC15A4), which plays an important role in the regulation of endolysosome-mediated signals, is a promising therapeutic target for several inflammatory diseases, including autoimmune diseases. The mechanisms by which SLC15A4 regulates inflammatory responses may provide a proof of concept for the efficacy of therapeutic strategies targeting immune cell endolysosomes.

Induction of steatosis in primary human hepatocytes recapitulates key pathophysiological aspects of metabolic dysfunction-associated steatotic liver disease.

BACKGROUND & AIMS: Metabolic dysfunction-associated steatotic liver disease (MASLD) is the most common cause of chronic liver disease. Its limited treatment options warrant novel pre-clinical models for target selection and drug validation. We have established and extensively characterized a primary human steatotic hepatocyte in vitro model system that could guide treatment strategies for MASLD. METHODS: Cryopreserved primary human hepatocytes from five donors varying in sex and ethnicity were cultured with free fatty acids (FFA) in 3D collagen sandwich for 7 days and the development of MASLD was followed by assessing classical hepatocellular functions. As proof of concept, the effects of the drug Firsocostat (GS-0976) on in vitro MASLD phenotypes were evaluated. RESULTS: Incubation with FFA induced steatosis, insulin resistance, mitochondrial dysfunction, inflammation, and alterations in prominent human gene signatures similar to patients with MASLD, indicating the recapitulation of human MASLD in this system. As the application of Firsocostat rescued clinically observed fatty liver disease pathologies, it highlights the ability of the in vitro system to test drug efficacy and potentially characterize their mode of action. CONCLUSIONS: Altogether, our human MASLD in vitro model system could guide the development and validation of novel targets and drugs for the treatment of MASLD. IMPACT AND IMPLICATIONS: Due to low drug efficacy and high toxicity, a clinical treatment option for MASLD is limited. To facilitate earlier stop-go decisions in drug development, we have established a primary human steatotic hepatocyte in vitro model. As the model recapitulates clinically relevant MASLD characteristics at high phenotypic resolution, it can serve as a pre-screening platform and guide target identification and validation in MASLD therapy.

Evaluation of a Covalent Library of Diverse Warheads (CovLib) Binding to JNK3, USP7, or p53.

Purpose: Over the last few years, covalent fragment-based drug discovery has gained significant importance. Thus, striving for more warhead diversity, we conceived a library consisting of 20 covalently reacting compounds. Our covalent fragment library (CovLib) contains four different warhead classes, including five α-cyanoacacrylamides/acrylates (CA), three epoxides (EO), four vinyl sulfones (VS), and eight electron-deficient heteroarenes with a leaving group (SNAr/SN). Methods: After predicting the theoretical solubility of the fragments by LogP and LogS during the selection process, we determined their experimental solubility using a turbidimetric solubility assay. The reactivities of the different compounds were measured in a high-throughput 5,5'-dithiobis-(2-nitrobenzoic acid) DTNB assay, followed by a (glutathione) GSH stability assay. We employed the CovLib in a (differential scanning fluorimetry) DSF-based screening against different targets: c-Jun N-terminal kinase 3 (JNK3), ubiquitin-specific protease 7 (USP7), and the tumor suppressor p53. Finally, the covalent binding was confirmed by intact protein mass spectrometry (MS). Results: In general, the purchased fragments turned out to be sufficiently soluble. Additionally, they covered a broad spectrum of reactivity. All investigated α-cyanoacrylamides/acrylates and all structurally confirmed epoxides turned out to be less reactive compounds, possibly due to steric hindrance and reversibility (for α-cyanoacrylamides/acrylates). The SNAr and vinyl sulfone fragments are either highly reactive or stable. DSF measurements with the different targets JNK3, USP7, and p53 identified reactive fragment hits causing a shift in the melting temperatures of the proteins. MS confirmed the covalent binding mode of all these fragments to USP7 and p53, while additionally identifying the SNAr-type electrophile SN002 as a mildly reactive covalent hit for p53. Conclusion: The screening and target evaluation of the CovLib revealed first interesting hits. The highly cysteine-reactive fragments VS004, SN001, SN006, and SN007 covalently modify several target proteins and showed distinct shifts in the melting temperatures up to +5.1 °C and -9.1 °C.

An expedited screening platform for the discovery of anti-ageing compounds in vitro and in vivo.

BACKGROUND: Restraining or slowing ageing hallmarks at the cellular level have been proposed as a route to increased organismal lifespan and healthspan. Consequently, there is great interest in anti-ageing drug discovery. However, this currently requires laborious and lengthy longevity analysis. Here, we present a novel screening readout for the expedited discovery of compounds that restrain ageing of cell populations in vitro and enable extension of in vivo lifespan. METHODS: Using Illumina methylation arrays, we monitored DNA methylation changes accompanying long-term passaging of adult primary human cells in culture. This enabled us to develop, test, and validate the CellPopAge Clock, an epigenetic clock with underlying algorithm, unique among existing epigenetic clocks for its design to detect anti-ageing compounds in vitro. Additionally, we measured markers of senescence and performed longevity experiments in vivo in Drosophila, to further validate our approach to discover novel anti-ageing compounds. Finally, we bench mark our epigenetic clock with other available epigenetic clocks to consolidate its usefulness and specialisation for primary cells in culture. RESULTS: We developed a novel epigenetic clock, the CellPopAge Clock, to accurately monitor the age of a population of adult human primary cells. We find that the CellPopAge Clock can detect decelerated passage-based ageing of human primary cells treated with rapamycin or trametinib, well-established longevity drugs. We then utilise the CellPopAge Clock as a screening tool for the identification of compounds which decelerate ageing of cell populations, uncovering novel anti-ageing drugs, torin2 and dactolisib (BEZ-235). We demonstrate that delayed epigenetic ageing in human primary cells treated with anti-ageing compounds is accompanied by a reduction in senescence and ageing biomarkers. Finally, we extend our screening platform in vivo by taking advantage of a specially formulated holidic medium for increased drug bioavailability in Drosophila. We show that the novel anti-ageing drugs, torin2 and dactolisib (BEZ-235), increase longevity in vivo. CONCLUSIONS: Our method expands the scope of CpG methylation profiling to accurately and rapidly detecting anti-ageing potential of drugs using human cells in vitro, and in vivo, providing a novel accelerated discovery platform to test sought after anti-ageing compounds and geroprotectors.

Structure-based mapping of the histone-binding pocket of KDM4D using functionalized tetrazole and pyridine core compounds.

KDM4 histone demethylases became an exciting target for inhibitor development as the evidence linking them directly to tumorigenesis mounts. In this study, we set out to better understand the binding cavity using an X-ray crystallographic approach to provide a detailed landscape of possible interactions within the under-investigated region of KDM4. Our design strategy was based on utilizing known KDM binding motifs, such as nicotinic acid and tetrazolylhydrazides, as core motifs that we decided to enrich with flexible tails to map the distal histone binding site. The resulting X-ray structures of the novel compounds bound to KDM4D, a representative of the KDM4 family, revealed the interaction pattern with distal residues in the histone-binding site. The most prominent protein rearrangement detected upon ligand binding is the loop movement that blocks the accessibility to the histone binding site. Apart from providing new sites that potential inhibitors can target, the novel compounds may prove helpful in exploring the capacity of ligands to bind in sites distal to the cofactor-binding site of other KDMs or 2-oxoglutarate (2OG)-dependent oxygenases. The case study proves that combining a strong small binding motif with flexible tails to probe the binding pocket will facilitate lead discovery in classical drug-discovery campaigns, given the ease of accessing X-ray quality crystals.

Antifungal Activity of 3-Hydrazinoquinoxaline-2-Thiol, a Novel Quinoxaline Derivative against Candida Species.

Candida ranks as among the most frequently encountered fungal infections that associated with high morbidity and mortality. Quinoxaline derivatives are a group of small molecules that showed a promising antimicrobial activity. This study aimed to investigate the fungicidal effects of 3-hydrazinoquinoxaline-2-thiol against Candida in comparison with Amphotericin B in vitro as a reference. Also, we aim to assess the efficacy of 3-hydrazinoquinoxaline-2-thiol in vivo using mice oral candidiasis model. Fifty-six Candida isolates were subjected to susceptibility testing by broth microdilution method for 3-hydrazinoquinoxaline-2-thiol and Amphotericin B. Therefore, Minimal inhibitory concentrations (MIC) were assessed and compared. The oral candidiasis mice model was used to evaluate the activity of 3-hydrazinoquinoxaline-2-thiol in vivo. Microbiological evaluation of progression and ELISA were used in this study. 3-hydrazinoquinoxaline-2-thiol was more effective than Amphotericin B against most clinical isolates of Candida albicans. Higher effectiveness was seen against Candida glabrata and Candida parapsilosis isolates. However, the efficiency against Candida tropicalis isolates varies. 3-hydrazinoquinoxaline-2-thiol was also effective against Pichia kudriavzevii and Clavispora lusitaniae. 3-hydrazinoquinoxaline-2-thiol showed a good efficacy in mice model against C. albicans cells ATCC 10231. 3-hydrazinoquinoxaline-2-thiol has shown promising antifungal and anti-inflammatory activity against different Candida species. More tests and experiments are needed.

Many locks to one key: N-acetylneuraminic acid binding to proteins.

Sialic acids play crucial roles in cell surface glycans of both eukaryotic and prokaryotic organisms, mediating various biological processes, including cell-cell interactions, development, immune response, oncogenesis and host-pathogen interactions. This review focuses on the ß-anomeric form of N-acetylneuraminic acid (Neu5Ac), particularly its binding affinity towards various proteins, as elucidated by solved protein structures. Specifically, we delve into the binding mechanisms of Neu5Ac to proteins involved in sequestering and transporting Neu5Ac in Gram-negative bacteria, with implications for drug design targeting these proteins as antimicrobial agents. Unlike the initial assumptions, structural analyses revealed significant variability in the Neu5Ac binding pockets among proteins, indicating diverse evolutionary origins and binding modes. By comparing these findings with existing structures from other systems, we can effectively highlight the intricate relationship between protein structure and Neu5Ac recognition, emphasizing the need for tailored drug design strategies to inhibit Neu5Ac-binding proteins across bacterial species.