This review explores the emerging potential of theranostic approaches in the pulmonary delivery of antimicrobial agents, with particular attention to recent FDA warnings concerning inhaled antifungal therapies. Pulmonary infections remain difficult to treat effectively due to the limitations of systemic drug delivery, anatomical and physiological barriers within the lungs, and microbial strategies that promote colonization. Inhaled drug delivery offers a targeted alternative but faces significant challenges, including the inherent variability of lung anatomy, disease-induced pulmonary alterations, and host defence mechanisms. We examine the crucial role of lung imaging in enabling theranostic applications, emphasizing magnetic resonance imaging (MRI) as the most promising modality due to its ability to provide non-invasive, radiation-free, and repeatable assessments of drug deposition. Within this context, the use of superparamagnetic iron oxide nanoparticles (SPIONs) as MRI contrast agents is critically assessed. SPIONs offer a safer alternative to gadolinium-based agents and hold considerable promise for improving the precision of imaging and treatment monitoring in the lungs. The article also outlines the significant regulatory barriers to the development and clinical adoption of inhaled antimicrobial therapies. These include the lack of standardized patient selection criteria, poorly defined clinical endpoints, and the inherent complexity of trial design for heterogeneous patient populations. To address these issues, we propose a conceptual framework for translating inhaled theranostic formulations into personalized antimicrobial therapies. This includes individualized dose adjustments based on imaging data and real-time monitoring of drug concentrations at the infection site. Such a tailored approach could significantly enhance treatment outcomes and meet the urgent clinical need for safer, more effective inhaled antimicrobial treatments.

Nanodiamonds (NDs) have emerged as a highly promising nanomaterial due to their intrinsic biocompatibility and remarkable antimicrobial and anti-adhesive properties, which result from their unique surface morphology. NDs serve as an excellent platform for extensive functionalization with diverse chemical groups and complex bioactive molecules, including peptides, photosensitizers, antibiotics and polycations. The antimicrobial potential of NDs has gained considerable attention in recent years across numerous application areas, including drug-delivery platforms, wound dressings, dentistry, surface coatings, biomedical implants, the food industry and water treatment technologies. This article compiles and critically evaluates the current microbiological evidence on ND antimicrobial activity. However, translating these findings into practical guidelines remains challenging due to the wide variability in reported results and the limited diversity of bacterial strains employed. The antimicrobial mechanisms of NDs in the context of Gram positive, Gram negative, and flagellated bacteria are examined, and it is demonstrated that key factors, including particle size, surface charge, and the composition of testing media, profoundly influence experimental outcomes and underlie many apparent contradictions in the field. Moreover, this review summarizes the functionalization strategies available for NDs, their reported biomedical and industrial applications, and current knowledge regarding their cytotoxicity and biocompatibility. Collectively, the article provides an integrated view of the structure-activity relationship governing ND antimicrobial performance.

Background: This study delves into the potential use of real-time UV imaging of the dissolution process combined with convolutional neural networks (CNNs) to develop multidimensional models representing the relation between in vitro and in vivo performance of drugs. Method: We utilised the capabilities of the SDi2 apparatus (Pion) to capture multidimensional dissolution data for two distinct Glucophage tablets: immediate-release 500 mg tablets and extended-release 750 mg tablets. The dissolution process was studied in various media, including a compendial pH 1.2 HCl solution, reverse osmosis water, and pH 6.8 phosphate buffer. Result: Moreover, results were captured at different wavelengths (255 nm and 520 nm) to provide a comprehensive view of the process. Our investigation focuses on two primary approaches: (1) analysing numerical data extracted from SDi2 images via a surface characterisation tool, using traditional machine learning techniques, including Scikit-learn, Tensorflow, and AutoML, and (2) utilising raw SDi2 images to train CNNs for direct prediction of in vivo metformin plasma concentrations. Conclusions: This dual approach allows us to assess the impact of data extraction on model performance and explore the potential of CNNs to capture complex dissolution patterns directly from images, potentially revealing hidden information not captured by traditional numerical data extraction methods.