Expanding the search for small-molecule antibacterials by multidimensional profiling

Identifying compound-protein interactions with knowledge graph embedding of perturbation transcriptomics

The emergence of perturbation transcriptomics provides a new perspective for drug discovery, but existing analysis methods suffer from inadequate performance and limited applicability. In this work, we present PertKGE, a method designed to deconvolute compound-protein interactions from perturbation transcriptomics with knowledge graph embedding. By considering multi-level regulatory events within biological systems that share the same semantic context, PertKGE significantly improves deconvoluting accuracy in two critical "cold-start" settings: inferring targets for new compounds and conducting virtual screening for new targets. We further demonstrate the pivotal role of incorporating multi-level regulatory events in alleviating representational biases. Notably, it enables the identification of ectonucleotide pyrophosphatase/phosphodiesterase-1 as the target responsible for the unique anti-tumor immunotherapy effect of tankyrase inhibitor K-756 and the discovery of five novel hits targeting the emerging cancer therapeutic target aldehyde dehydrogenase 1B1 with a remarkable hit rate of 10.2%. These findings highlight the potential of PertKGE to accelerate drug discovery.

A chemically engineered water-soluble block copolymer for redox responsive SO2 release in antibacterial therapy

Sulfur dioxide (SO2) has emerged as a promising gasotransmitter for various therapeutic applications, including antibacterial activities. However, the potential of polymeric SO2 donors for antimicrobial activities remains largely unexplored. Herein, we report a water-soluble, redox-responsive, SO2-releasing amphiphilic block copolymer poly(polyethylene glycol methyl ether methacrylate) (PPEGMA)-b-poly(2-((2,4-dinitrophenyl)sulfonamido)ethyl methacrylate (PM)) (BCPx) to investigate their antibacterial properties. BCPx contains hydrophilic polyethylene glycol (PEG) pendants and a hydrophobic SO2-releasing PM block, facilitating the formation of self-assembled nanoparticles (BCPxNp) in an aqueous medium, studied by critical aggregation concentration (CAC) measurements, dynamic light scattering (DLS), field emission scanning electron microscopy (FESEM) and transmission electron microscopy (TEM). BCPxNp exhibits sustained SO2 release up to 12 h in the presence of glutathione (GSH), with a yield of 30-80% of theoretical SO2 release. In vitro antibacterial studies unveil the outstanding antibacterial activity of BCP3Np against Gram-positive bacteria Bacillus subtilis, as evidenced by FESEM and live/dead cell fluorescence assay. We further elucidate the antibacterial mechanism through reactive oxygen species (ROS) generation studies. Overall, the polymer exhibits excellent biocompatibility at effective antimicrobial concentrations and provides insights into the design of a new class of SO2-releasing polymeric antibacterial agents.

AI-guided few-shot inverse design of HDP-mimicking polymers against drug-resistant bacteria

Host defense peptide (HDP)-mimicking polymers are promising therapeutic alternatives to antibiotics and have large-scale untapped potential. Artificial intelligence (AI) exhibits promising performance on large-scale chemical-content design, however, existing AI methods face difficulties on scarcity data in each family of HDP-mimicking polymers (<102), much smaller than public polymer datasets (>105), and multi-constraints on properties and structures when exploring high-dimensional polymer space. Herein, we develop a universal AI-guided few-shot inverse design framework by designing multi-modal representations to enrich polymer information for predictions and creating a graph grammar distillation for chemical space restriction to improve the efficiency of multi-constrained polymer generation with reinforcement learning. Exampled with HDP-mimicking β-amino acid polymers, we successfully simulate predictions of over 105 polymers and identify 83 optimal polymers. Furthermore, we synthesize an optimal polymer DM0.8iPen0.2 and find that this polymer exhibits broad-spectrum and potent antibacterial activity against multiple clinically isolated antibiotic-resistant pathogens, validating the effectiveness of AI-guided design strategy.

Dynamic Hydrogels against Infections: From Design to Applications

Human defense against infection remains a global topic. In addition to developing novel anti-infection drugs, therapeutic drug delivery strategies are also crucial to achieving a higher efficacy and lower toxicity of these drugs for treatment. The application of hydrogels has been proven to be an effective localized drug delivery approach to treating infections without generating significant systemic adverse effects. The recent emerging dynamic hydrogels further show power as injectable formulations, giving new tools for clinical treatments. In this review, we delve into the potential applications of dynamic hydrogels in antibacterial and antiviral treatments and elaborate on their molecular designs and practical implementations. By outlining the chemical designs underlying these hydrogels, we discuss how the choice of dynamic chemical bonds affects their stimulus responsiveness, self-healing capabilities, and mechanical properties. Afterwards, we focus on how to endow dynamic hydrogels with anti-infection properties. By comparing different drug-loading methods, we highlight the advantages of dynamic chemical bonds in achieving sustained and controlled drug release. Moreover, we also include the design principles and uses of hydrogels that possess inherent anti-infective properties. Furthermore, we explore the design principles and applications of hydrogels with inherent anti-infective properties. Finally, we briefly summarize the current challenges faced by dynamic hydrogels and present a forward-looking vision for their future development. Through this review, we expect to draw more attention to these therapeutic strategies among scientists working with chemistry, materials, as well as pharmaceutics.

Engineered Bio-Heterojunction Confers Extra- and Intracellular Bacterial Ferroptosis and Hunger-Triggered Cell Protection for Diabetic Wound Repair

Nanomaterial-mediated ferroptosis has garnered considerable interest in the antibacterial field, as it invokes the disequilibrium of ion homeostasis and boosts lipid peroxidation in extra- and intracellular bacteria. However, current ferroptosis-associated antibacterial strategies indiscriminately pose damage to healthy cells, ultimately compromising their biocompatibility. To address this daunting issue, this work has designed a precise ferroptosis bio-heterojunction (F-bio-HJ) consisting of Fe2 O3 , Ti3 C2 -MXene, and glucose oxidase (GOx) to induce extra-intracellular bacteria-targeted ferroptosis for infected diabetic cutaneous regeneration. Fe2 O3 /Ti3 C2 -MXene@GOx (FMG) catalytically generates a considerable amount of ROS which assaults the membrane of extracellular bacteria, facilitating the permeation of synchronously generated Fe2+ /Fe3+ into bacteria under near-infrared (NIR) irradiation, causing planktonic bacterial death via ferroptosis, Fe2+ overload, and lipid peroxidation. Additionally, FMG facilitates intracellular bacterial ferroptosis by transporting Fe2+ into intracellular bacteria via inward ferroportin (FPN). With GOx consuming glucose, FMG creates hunger protection which helps macrophages escape cell ferroptosis by activating the adenosine 5'-monophosphate (AMP) activated protein kinase (AMPK) pathway. In vivo results authenticate that FMG boosts diabetic infectious cutaneous regeneration without triggering ferroptosis in normal cells. As envisaged, the proposed tactic provides a promising approach to combat intractable infections by precisely terminating extra-intracellular infection via steerable ferroptosis, thereby markedly elevating the biocompatibility of therapeutic ferroptosis-mediated strategies.

Antimicrobial resistance crisis: could artificial intelligence be the solution?

Antimicrobial resistance is a global public health threat, and the World Health Organization (WHO) has announced a priority list of the most threatening pathogens against which novel antibiotics need to be developed. The discovery and introduction of novel antibiotics are time-consuming and expensive. According to WHO's report of antibacterial agents in clinical development, only 18 novel antibiotics have been approved since 2014. Therefore, novel antibiotics are critically needed. Artificial intelligence (AI) has been rapidly applied to drug development since its recent technical breakthrough and has dramatically improved the efficiency of the discovery of novel antibiotics. Here, we first summarized recently marketed novel antibiotics, and antibiotic candidates in clinical development. In addition, we systematically reviewed the involvement of AI in antibacterial drug development and utilization, including small molecules, antimicrobial peptides, phage therapy, essential oils, as well as resistance mechanism prediction, and antibiotic stewardship.

Bioinspired silver nanoparticle-based nanocomposites for effective control of plant pathogens: A review

Plant pathogens, including bacteria, fungi, and viruses, pose significant challenges to the farming community due to their extensive diversity, the rapidly evolving phenomenon of multi-drug resistance (MDR), and the limited availability of effective control measures. Amid mounting global pressure, particularly from the World Health Organization, to limit the use of antibiotics in agriculture and livestock management, there is increasing consideration of engineered nanomaterials (ENMs) as promising alternatives for antimicrobial applications. Studies focusing on the application of ENMs in the fight against MDR pathogens are receiving increasing attention, driven by significant losses in agriculture and critical knowledge gaps in this crucial field. In this review, we explore the potential contributions of silver nanoparticles (AgNPs) and their nanocomposites in combating plant diseases, within the emerging interdisciplinary arena of nano-phytopathology. AgNPs and their nanocomposites are increasingly acknowledged as promising countermeasures against plant pathogens, owing to their unique physicochemical characteristics and inherent antimicrobial properties. This review explores recent advancements in engineered nanocomposites, highlights their diverse mechanisms for pathogen control, and draws attention to their potential in antibacterial, antifungal, and antiviral applications. In the discussion, we briefly address three crucial dimensions of combating plant pathogens: green synthesis approaches, toxicity-environmental concerns, and factors influencing antimicrobial efficacy. Finally, we outline recent advancements, existing challenges, and prospects in scholarly research to facilitate the integration of nanotechnology across interdisciplinary fields for more effective treatment and prevention of plant diseases.

Sensing of Antibiotic-Bacteria Interactions

Sensing of antibiotic-bacteria interactions is an important area of research that has gained significant attention in recent years. Antibiotic resistance is a major public health concern, and it is essential to develop new strategies for detecting and monitoring bacterial responses to antibiotics in order to maintain effective antibiotic development and antibacterial treatment. This review summarizes recent advances in sensing strategies for antibiotic-bacteria interactions, which are divided into two main parts: studies on the mechanism of action for sensitive bacteria and interrogation of the defense mechanisms for resistant ones. In conclusion, this review provides an overview of the present research landscape concerning antibiotic-bacteria interactions, emphasizing the potential for method adaptation and the integration of machine learning techniques in data analysis, which could potentially lead to a transformative impact on mechanistic studies within the field.

The critical role of mode of action studies in kinetoplastid drug discovery

Understanding the target and mode of action of compounds identified by phenotypic screening can greatly facilitate the process of drug discovery and development. Here, we outline the tools currently available for target identification against the neglected tropical diseases, human African trypanosomiasis, visceral leishmaniasis and Chagas' disease. We provide examples how these tools can be used to identify and triage undesirable mechanisms, to identify potential toxic liabilities in patients and to manage a balanced portfolio of target-based campaigns. We review the primary targets of drugs that are currently in clinical development that were initially identified via phenotypic screening, and whose modes of action affect protein turnover, RNA trans-splicing or signalling in these protozoan parasites.

Accelerating therapeutic protein design with computational approaches toward the clinical stage

Therapeutic protein, represented by antibodies, is of increasing interest in human medicine. However, clinical translation of therapeutic protein is still largely hindered by different aspects of developability, including affinity and selectivity, stability and aggregation prevention, solubility and viscosity reduction, and deimmunization. Conventional optimization of the developability with widely used methods, like display technologies and library screening approaches, is a time and cost-intensive endeavor, and the efficiency in finding suitable solutions is still not enough to meet clinical needs. In recent years, the accelerated advancement of computational methodologies has ushered in a transformative era in the field of therapeutic protein design. Owing to their remarkable capabilities in feature extraction and modeling, the integration of cutting-edge computational strategies with conventional techniques presents a promising avenue to accelerate the progression of therapeutic protein design and optimization toward clinical implementation. Here, we compared the differences between therapeutic protein and small molecules in developability and provided an overview of the computational approaches applicable to the design or optimization of therapeutic protein in several developability issues.