[Discovery of a New Siderophore Cephalosporin, Cefiderocol].

Cefiderocol is a novel siderophore-conjugated cephalosporin with a catechol residue acting as an iron chelator. Cefiderocol forms a chelating complex with ferric iron and is transported rapidly into bacterial cells through iron-uptake systems. As a result, cefiderocol shows good activity against Gram-negative bacteria, including carbapenem-resistant isolates that are causing significant global health issues. Cefiderocol has been approved for clinical use in the United States and Europe, where it is being used to treat infection caused by carbapenem-resistant Gram-negative pathogens.

[New Drug Discovery Targeting Iron in Bacterial Infectious Diseases].

Iron is necessary for all living organisms, and bacteria that cause infections in human hosts also need ferrous ions for their growth and proliferation. In the human body, most ferric ions (Fe3+) are tightly bound to iron-binding proteins such as hemoglobin, transferrin, lactoferrin, and ferritin. Pathogenic bacteria express highly specific iron uptake systems, including siderophores and specific receptors. Most bacteria secrete siderophores, which are low-molecular weight metal-chelating agents, to capture Fe3+ outside cell. Siderophores are mainly classified as either catecholate or hydroxamate. Vibrio vulnificus, a Gram-negative pathogenic bacterium, is responsible for serious infections in humans and requires iron for growth. A clinical isolate, V. vulnificus M2799, secretes a catecholate siderophore, vulnibactin, that captures ferric ions from the environment. In our study, we generated deletion mutants of the genes encoding proteins involved in the vulnibactin mediated iron-utilization system, such as ferric-vulnibactin receptor protein (VuuA), periplasmic ferric-vulnibactin binding protein (FatB), ferric-vulnibactin reductase (VuuB), and isochorismate synthase (ICS). ICS and VuuA are required under low-iron conditions for ferric-utilization in M2799, but the alternative proteins FatB and VuuB can function as a periplasmic binding protein and a ferric-chelate reductase, respectively. VatD, which functions as ferric-hydroxamate siderophores periplasmic binding protein, was shown to participate in the ferric-vulnibactin uptake system in the absence of FatB. Furthermore, the ferric-hydroxamate siderophore reductase IutB was observed to participate in ferric-vulnibactin reduction in the absence of VuuB. We propose that ferric-siderophore periplasmic binding proteins and ferric-chelate reductases represent potential targets for drug discovery in the context of infectious diseases.

Thermostability-assisted limited proteolysis-coupled mass spectrometry for capturing drug target proteins and sites.

BACKGROUND: Identifying drug-binding targets and their corresponding sites is crucial for drug discovery and mechanism studies. Limited proteolysis-coupled mass spectrometry (LiP-MS) is a sophisticated method used for the detection of compound and protein interactions. However, in some cases, LiP-MS cannot identify the target proteins due to the small structure changes or the lack of enrichment of low-abundant protein. To overcome this drawback, we developed a thermostability-assisted limited proteolysis-coupled mass spectrometry (TALiP-MS) approach for efficient drug target discovery. RESULTS: We proved that the novel strategy, TALiP-MS, could efficiently identify target proteins of various ligands, including cyclosporin A (a calcineurin inhibitor), geldanamycin (an HSP90 inhibitor), and staurosporine (a kinase inhibitor), with accurately recognizing drug-binding domains. The TALiP protocol increased the number of target peptides detected in LiP-MS experiments by 2- to 8-fold. Meanwhile, the TALiP-MS approach can not only identify both ligand-binding stability and destabilization proteins but also shows high complementarity with the thermal proteome profiling (TPP) and machine learning-based limited proteolysis (LiP-Quant) methods. The developed TALiP-MS approach was applied to identify the target proteins of celastrol (CEL), a natural product known for its strong antioxidant and anti-cancer angiogenesis effect. Among them, four proteins, MTHFD1, UBA1, ACLY, and SND1 were further validated for their strong affinity to CEL by using cellular thermal shift assay. Additionally, the destabilized proteins induced by CEL such as TAGLN2 and CFL1 were also validated. SIGNIFICANCE: Collectively, these findings underscore the efficacy of the TALiP-MS method for identifying drug targets, elucidating binding sites, and even detecting drug-induced conformational changes in target proteins in complex proteomes.

Plant-Derived Peptides: (Neglected) Natural Products for Drug Discovery.

Peptides have emerged as key regulators in various physiological processes, including growth, development, stress, and defense responses within plants as well as ecological interactions of plants with microbes and animals. Understanding and harnessing plant peptides can lead to the development of innovative strategies for crop improvement, increasing agricultural productivity, and enhancing resilience to environmental challenges such as drought, pests, and diseases. Moreover, some plant peptides have shown promise in human health applications, with potential therapeutic benefits as ingredients in herbal medicines as well as novel drug leads. The exploration of plant peptides is essential for unraveling the mysteries of plant biology and advancing peptide drug discovery. This short personal commentary provides a very brief overview about the field of plant-derived peptides and a personal word of motivation to increase the number of scientists in pharmacognosy working with these fascinating biomolecules.

Application of 3Rs in Caenorhabditis elegans Research for the Identification of Health-Promoting Natural Products.

The average age of the population is increasing worldwide, which has a profound impact on our society. This leads to an increasing demand for medicines and requires the development of new strategies to promote health during the additional years. In the search for resources and therapeutics for improved health during an extended life span, attention has to be paid to environmental exposure and ecosystem burdens that inevitably emerge with the extended consumption of medicines and drug development, even in the preclinical stage. The hereby introduced sustainable strategy for drug discovery is built on 3Rs, "R: obustness, R: eliability, and saving R: esources", inspired by both the 3Rs used in animal experiments and environmental protection, and centers on the usefulness and the variety of the small model organism Caenorhabditis elegans for detecting health-promoting natural products. A workflow encompassing a multilevel screening approach is presented to maximize the amount of information on health-promoting samples, while considering the 3Rs. A detailed, methodology- and praxis-oriented compilation and discussion of proposed C. elegans health span assays and more disease-specific assays are presented to offer guidance for scientists intending to work with C. elegans, thus facilitating the initial steps towards the integration of C. elegans assays in their laboratories.

The Pan-Canadian Chemical Library: A Mechanism to Open Academic Chemistry to High-Throughput Virtual Screening.

Computationally screening chemical libraries to discover molecules with desired properties is a common technique used in early-stage drug discovery. Recent progress in the field now enables the efficient exploration of billions of molecules within days or hours, but this exploration remains confined within the boundaries of the accessible chemistry space. While the number of commercially available compounds grows rapidly, it remains a limited subset of all druglike small molecules that could be synthesized. Here, we present a workflow where chemical reactions typically developed in academia and unconventional in drug discovery are exploited to dramatically expand the chemistry space accessible to virtual screening. We use this process to generate a first version of the Pan-Canadian Chemical Library, a collection of nearly 150 billion diverse compounds that does not overlap with other ultra-large libraries such as Enamine REAL or SAVI and could be a resource of choice for protein targets where other libraries have failed to deliver bioactive molecules.

Large-scale data mining of four billion human antibody variable regions reveals convergence between therapeutic and natural antibodies that constrains search space for biologics drug discovery.

The naïve human antibody repertoire has theoretical access to an estimated > 1015 antibodies. Identifying subsets of this prohibitively large space where therapeutically relevant antibodies may be found is useful for development of these agents. It was previously demonstrated that, despite the immense sequence space, different individuals can produce the same antibodies. It was also shown that therapeutic antibodies, which typically follow seemingly unnatural development processes, can arise independently naturally. To check for biases in how the sequence space is explored, we data mined public repositories to identify 220 bioprojects with a combined seven billion reads. Of these, we created a subset of human bioprojects that we make available as the AbNGS database (https://naturalantibody.com/ngs/). AbNGS contains 135 bioprojects with four billion productive human heavy variable region sequences and 385 million unique complementarity-determining region (CDR)-H3s. We find that 270,000 (0.07% of 385 million) unique CDR-H3s are highly public in that they occur in at least five of 135 bioprojects. Of 700 unique therapeutic CDR-H3, a total of 6% has direct matches in the small set of 270,000. This observation extends to a match between CDR-H3 and V-gene call as well. Thus, the subspace of shared ('public') CDR-H3s shows utility for serving as a starting point for therapeutic antibody design.

Precision cut lung slices: an integrated ex vivo model for studying lung physiology, pharmacology, disease pathogenesis and drug discovery.

Precision Cut Lung Slices (PCLS) have emerged as a sophisticated and physiologically relevant ex vivo model for studying the intricacies of lung diseases, including fibrosis, injury, repair, and host defense mechanisms. This innovative methodology presents a unique opportunity to bridge the gap between traditional in vitro cell cultures and in vivo animal models, offering researchers a more accurate representation of the intricate microenvironment of the lung. PCLS require the precise sectioning of lung tissue to maintain its structural and functional integrity. These thin slices serve as invaluable tools for various research endeavors, particularly in the realm of airway diseases. By providing a controlled microenvironment, precision-cut lung slices empower researchers to dissect and comprehend the multifaceted interactions and responses within lung tissue, thereby advancing our understanding of pulmonary pathophysiology.

Cell microencapsulation techniques for cancer modelling and drug discovery.

Cell encapsulation into spherical microparticles is a promising bioengineering tool in many fields, including 3D cancer modelling and pre-clinical drug discovery. Cancer microencapsulation models can more accurately reflect the complex solid tumour microenvironment than 2D cell culture and therefore would improve drug discovery efforts. However, these microcapsules, typically in the range of 1 - 5000 µm in diameter, must be carefully designed and amenable to high-throughput production. This review therefore aims to outline important considerations in the design of cancer cell microencapsulation models for drug discovery applications and examine current techniques to produce these. Extrusion (dripping) droplet generation and emulsion-based techniques are highlighted and their suitability to high-throughput drug screening in terms of tumour physiology and ease of scale up is evaluated.

3D microencapsulation models of cancer offer a customisable platform to mimic key aspects of solid tumour physiology in vitro. However, many 3D models do not recapitulate the hypoxic conditions and altered tissue stiffness established in many tumour types and stages. Furthermore, microparticles for cancer cell encapsulation are commonly produced using methods that are not necessarily suitable for scale up to high-throughput manufacturing. This review aims to evaluate current technologies for cancer cell-laden microparticle production with a focus on physiological relevance and scalability. Emerging techniques will then be touched on, for production of uniform microparticles suitable for high-throughput drug discovery applications.

Artificial Intelligence in Drug Identification and Validation: A Scoping Review.

The end-to-end process in the discovery of drugs involves therapeutic candidate identification, validation of identified targets, identification of hit compound series, lead identification and optimization, characterization, and formulation and development. The process is lengthy, expensive, tedious, and inefficient, with a large attrition rate for novel drug discovery. Today, the pharmaceutical industry is focused on improving the drug discovery process. Finding and selecting acceptable drug candidates effectively can significantly impact the price and profitability of new medications. Aside from the cost, there is a need to reduce the end-to-end process time, limiting the number of experiments at various stages. To achieve this, artificial intelligence (AI) has been utilized at various stages of drug discovery. The present study aims to identify the recent work that has developed AI-based models at various stages of drug discovery, identify the stages that need more concern, present the taxonomy of AI methods in drug discovery, and provide research opportunities. From January 2016 to September 1, 2023, the study identified all publications that were cited in the electronic databases including Scopus, NCBI PubMed, MEDLINE, Anthropology Plus, Embase, APA PsycInfo, SOCIndex, and CINAHL. Utilising a standardized form, data were extracted, and presented possible research prospects based on the analysis of the extracted data.

DCGAN-DTA: Predicting drug-target binding affinity with deep convolutional generative adversarial networks.

BACKGROUND: In recent years, there has been a growing interest in utilizing computational approaches to predict drug-target binding affinity, aiming to expedite the early drug discovery process. To address the limitations of experimental methods, such as cost and time, several machine learning-based techniques have been developed. However, these methods encounter certain challenges, including the limited availability of training data, reliance on human intervention for feature selection and engineering, and a lack of validation approaches for robust evaluation in real-life applications. RESULTS: To mitigate these limitations, in this study, we propose a method for drug-target binding affinity prediction based on deep convolutional generative adversarial networks. Additionally, we conducted a series of validation experiments and implemented adversarial control experiments using straw models. These experiments serve to demonstrate the robustness and efficacy of our predictive models. We conducted a comprehensive evaluation of our method by comparing it to baselines and state-of-the-art methods. Two recently updated datasets, namely the BindingDB and PDBBind, were used for this purpose. Our findings indicate that our method outperforms the alternative methods in terms of three performance measures when using warm-start data splitting settings. Moreover, when considering physiochemical-based cold-start data splitting settings, our method demonstrates superior predictive performance, particularly in terms of the concordance index. CONCLUSION: The results of our study affirm the practical value of our method and its superiority over alternative approaches in predicting drug-target binding affinity across multiple validation sets. This highlights the potential of our approach in accelerating drug repurposing efforts, facilitating novel drug discovery, and ultimately enhancing disease treatment. The data and source code for this study were deposited in the GitHub repository, https://github.com/mojtabaze7/DCGAN-DTA . Furthermore, the web server for our method is accessible at https://dcgan.shinyapps.io/bindingaffinity/ .

Research progress of autoimmune diseases based on induced pluripotent stem cells.

Autoimmune diseases can damage specific or multiple organs and tissues, influence the quality of life, and even cause disability and death. A 'disease in a dish' can be developed based on patients-derived induced pluripotent stem cells (iPSCs) and iPSCs-derived disease-relevant cell types to provide a platform for pathogenesis research, phenotypical assays, cell therapy, and drug discovery. With rapid progress in molecular biology research methods including genome-sequencing technology, epigenetic analysis, '-omics' analysis and organoid technology, large amount of data represents an opportunity to help in gaining an in-depth understanding of pathological mechanisms and developing novel therapeutic strategies for these diseases. This paper aimed to review the iPSCs-based research on phenotype confirmation, mechanism exploration, drug discovery, and cell therapy for autoimmune diseases, especially multiple sclerosis, inflammatory bowel disease, and type 1 diabetes using iPSCs and iPSCs-derived cells.

Studies on Comprehensive Total Synthesis of Natural and Pseudo-Natural Products for Drug Discovery.

Natural products are important for the development of pharmaceuticals and agrochemicals; thus, their synthesis and medicinal chemistry research is critical. Developing a total synthesis pathway for natural products confirms their structure and provides the opportunity to modify the structure in a targeted manner. A simple modification of a single oxidation step can increase the biological activity, or the complexity of the molecule can alter the property. Herein, we discuss the asymmetric total synthesis of dihydroisocoumarin-type natural products, the creation of novel antibacterial compounds through partial structural modification, and the development of antioxidants with high activity and low toxicity through dimerization strategies.

Elucidating the semantics-topology trade-off for knowledge inference-based pharmacological discovery.

Leveraging AI for synthesizing the deluge of biomedical knowledge has great potential for pharmacological discovery with applications including developing new therapeutics for untreated diseases and repurposing drugs as emergent pandemic treatments. Creating knowledge graph representations of interacting drugs, diseases, genes, and proteins enables discovery via embedding-based ML approaches and link prediction. Previously, it has been shown that these predictive methods are susceptible to biases from network structure, namely that they are driven not by discovering nuanced biological understanding of mechanisms, but based on high-degree hub nodes. In this work, we study the confounding effect of network topology on biological relation semantics by creating an experimental pipeline of knowledge graph semantic and topological perturbations. We show that the drop in drug repurposing performance from ablating meaningful semantics increases by 21% and 38% when mitigating topological bias in two networks. We demonstrate that new methods for representing knowledge and inferring new knowledge must be developed for making use of biomedical semantics for pharmacological innovation, and we suggest fruitful avenues for their development.

Patient-derived tumoroids and proteomic signatures: tools for early drug discovery.

Onco-virotherapy is an emergent treatment for cancer based on viral vectors. The therapeutic activity is based on two different mechanisms including tumor-specific oncolysis and immunostimulatory properties. In this study, we evaluated onco-virotherapy in vitro responses on immunocompetent non-small cell lung cancer (NSCLC) patient-derived tumoroids (PDTs) and healthy organoids. PDTs are accurate tools to predict patient's clinical responses at the in vitro stage. We showed that onco-virotherapy could exert specific antitumoral effects by producing a higher number of viral particles in PDTs than in healthy organoids. In the present work, we used multiplex protein screening, based on proximity extension assay to highlight different response profiles. Our results pointed to the increase of proteins implied in T cell activation, such as IFN-γ following onco-virotherapy treatment. Based on our observation, oncolytic viruses-based therapy responders are dependent on several factors: a high PD-L1 expression, which is a biomarker of greater immune response under immunotherapies, and the number of viral particles present in tumor tissue, which is dependent to the metabolic state of tumoral cells. Herein, we highlight the use of PDTs as an alternative in vitro model to assess patient-specific responses to onco-virotherapy at the early stage of the preclinical phases.

Computational drug repositioning with attention walking.

Drug repositioning aims to identify new therapeutic indications for approved medications. Recently, the importance of computational drug repositioning has been highlighted because it can reduce the costs, development time, and risks compared to traditional drug discovery. Most approaches in this area use networks for systematic analysis. Inferring drug-disease associations is then defined as a link prediction problem in a heterogeneous network composed of drugs and diseases. In this article, we present a novel method of computational drug repositioning, named drug repositioning with attention walking (DRAW). DRAW proceeds as follows: first, a subgraph enclosing the target link for prediction is extracted. Second, a graph convolutional network captures the structural features of the labeled nodes in the subgraph. Third, the transition probabilities are computed using attention mechanisms and converted into random walk profiles. Finally, a multi-layer perceptron takes random walk profiles and predicts whether a target link exists. As an experiment, we constructed two heterogeneous networks with drug-drug similarities based on chemical structures and anatomical therapeutic chemical classification (ATC) codes. Using 10-fold cross-validation, DRAW achieved an area under the receiver operating characteristic (ROC) curve of 0.903 and outperformed state-of-the-art methods. Moreover, we demonstrated the results of case studies for selected drugs and diseases to further confirm the capability of DRAW to predict drug-disease associations.

Multicriteria decision making attributes and estimation of physicochemical properties of kidney cancer drugs via topological descriptors.

Based on topological descriptors, QSPR analysis is an incredibly helpful statistical method for examining many physical and chemical properties of compounds without demanding costly and time-consuming laboratory tests. Firstly, we discuss and provide research on kidney cancer drugs using topological indices and done partition of the edges of kidney cancer drugs which are based on the degree. Secondly, we examine the attributes of nineteen drugs casodex, eligard, mitoxanrone, rubraca, and zoladex, etc and among others, using linear QSPR model. The study in the article not only demonstrates a good correlation between TIs and physical characteristics with the QSPR model being the most suitable for predicting complexity, enthalpy, molar refractivity, and other factors and a best-fit model is attained in this study. This theoretical approach might benefit chemists and professionals in the pharmaceutical industry to forecast the characteristics of kidney cancer therapies. This leads towards new opportunities to paved the way for drug discovery and the formation of efficient and suitable treatment options in therapeutic targeting. We also employed multicriteria decision making techniques like COPRAS and PROMETHEE-II for ranking of said disease treatment drugs and physicochemical characteristics.