Ternary nanoparticle complex of antibiotic, polyelectrolyte, and mucolytic enzyme as a potential antibiotic delivery system in bronchiectasis therapy

The Respiratory Microbiome in Paediatric Chronic Wet Cough: What Is Known and Future Directions

Chronic wet cough for longer than 4 weeks is a hallmark of chronic suppurative lung diseases (CSLD), including protracted bacterial bronchitis (PBB), and bronchiectasis in children. Severe lower respiratory infection early in life is a major risk factor of PBB and paediatric bronchiectasis. In these conditions, failure to clear an underlying endobronchial infection is hypothesised to drive ongoing inflammation and progressive tissue damage that culminates in irreversible bronchiectasis. Historically, the microbiology of paediatric chronic wet cough has been defined by culture-based studies focused on the detection and eradication of specific bacterial pathogens. Various 'omics technologies now allow for a more nuanced investigation of respiratory pathobiology and are enabling development of endotype-based models of care. Recent years have seen substantial advances in defining respiratory endotypes among adults with CSLD; however, less is understood about diseases affecting children. In this review, we explore the current understanding of the airway microbiome among children with chronic wet cough related to the PBB-bronchiectasis diagnostic continuum. We explore concepts emerging from the gut-lung axis and multi-omic studies that are expected to influence PBB and bronchiectasis endotyping efforts. We also consider how our evolving understanding of the airway microbiome is translating to new approaches in chronic wet cough diagnostics and treatments.

Modulation of engineered nanomaterial interactions with organ barriers for enhanced drug transport

The biomedical use of nanoparticles (NPs) has been the focus of intense research for over a decade. As most NPs are explored as carriers to alter the biodistribution, pharmacokinetics and bioavailability of associated drugs, the delivery of these NPs to the tissues of interest remains an important topic. To date, the majority of NP delivery studies have used tumor models as their tool of interest, and the limitations concerning tumor targeting of systemically administered NPs have been well studied. In recent years, the focus has also shifted to other organs, each presenting their own unique delivery challenges to overcome. In this review, we discuss the recent advances in leveraging NPs to overcome four major biological barriers including the lung mucus, the gastrointestinal mucus, the placental barrier, and the blood-brain barrier. We define the specific properties of these biological barriers, discuss the challenges related to NP transport across them, and provide an overview of recent advances in the field. We discuss the strengths and shortcomings of different strategies to facilitate NP transport across the barriers and highlight some key findings that can stimulate further advances in this field.

Emerging Polymer-Based Nanosystem Strategies in the Delivery of Antifungal Drugs

Nanosystems-based antifungal agents have emerged as an effective strategy to address issues related to drug resistance, drug release, and toxicity. Among the diverse materials employed for antifungal drug delivery, polymers, including polysaccharides, proteins, and polyesters, have gained significant attention due to their versatility. Considering the complex nature of fungal infections and their varying sites, it is crucial for researchers to carefully select appropriate polymers based on specific scenarios when designing antifungal agent delivery nanosystems. This review provides an overview of the various types of nanoparticles used in antifungal drug delivery systems, with a particular emphasis on the types of polymers used. The review focuses on the application of drug delivery systems and the release behavior of these systems. Furthermore, the review summarizes the critical physical properties and relevant information utilized in antifungal polymer nanomedicine delivery systems and briefly discusses the application prospects of these systems.

Towards machine learning discovery of dual antibacterial drug-nanoparticle systems

Artificial Intelligence/Machine Learning (AI/ML) algorithms may speed up the design of DADNP systems formed by Antibacterial Drugs (AD) and Nanoparticles (NP). In this work, we used IFPTML = Information Fusion (IF) + Perturbation-Theory (PT) + Machine Learning (ML) algorithm for the first time to study of a large dataset of putative DADNP systems composed by >165 000 ChEMBL AD assays and 300 NP assays vs. multiple bacteria species. We trained alternative models with Linear Discriminant Analysis (LDA), Artificial Neural Networks (ANN), Bayesian Networks (BNN), K-Nearest Neighbour (KNN) and other algorithms. IFPTML-LDA model was simpler with values of Sp ≈ 90% and Sn ≈ 74% in both training (>124 K cases) and validation (>41 K cases) series. IFPTML-ANN and KNN models are notably more complicated even when they are more balanced Sn ≈ Sp ≈ 88.5%-99.0% and AUROC ≈ 0.94-0.99 in both series. We also carried out a simulation (>1900 calculations) of the expected behavior for putative DADNPs in 72 different biological assays. The putative DADNPs studied are formed by 27 different drugs with multiple classes of NP and types of coats. In addition, we tested the validity of our additive model with 80 DADNP complexes experimentally synthetized and biologically tested (reported in >45 papers). All these DADNPs show values of MIC < 50 μg mL^-1 (cutoff used) better that MIC of AD and NP alone (synergistic or additive effect). The assays involve DADNP complexes with 10 types of NP, 6 coating materials, NP size range 5-100 nm vs. 15 different antibiotics, and 12 bacteria species. The IFPTML-LDA model classified correctly 100% (80 out of 80) DADNP complexes as biologically active. IFPMTL additive strategy may become a useful tool to assist the design of DADNP systems for antibacterial therapy taking into consideration only information about AD and NP components by separate.