Antimicrobial resistance (AMR) remains an important global public health issue with antimicrobial misuse and overuse being one of the main drivers. The Global Point Prevalence Survey (G-PPS) of Antimicrobial Consumption and Resistance assesses the prevalence and the quality of antimicrobial prescriptions across hospitals globally. G-PPS was carried out at 17 hospitals across Ghana, Uganda, Zambia and Tanzania. The overall prevalence of antimicrobial use was 50% (30–57%), with most antibiotics prescribed belonging to the WHO ‘Access’ and ‘Watch’ categories. No ‘Reserve’ category of antibiotics was prescribed across the study sites while antimicrobials belonging to the ‘Not Recommended’ group were prescribed infrequently. Antimicrobials were most often prescribed for prophylaxis for obstetric or gynaecological surgery, making up between 12 and 18% of total prescriptions across all countries. The most prescribed therapeutic subgroup of antimicrobials was ‘Antibacterials for systemic use’. As a result of the programme, PPS data are now readily available for the first time in the hospitals, strengthening the global commitment to improved antimicrobial surveillance. Antimicrobial stewardship interventions developed included the formation of AMS committees, the provision of training and the preparation of new AMS guidelines. Other common interventions included the presentation of findings to clinicians for increased awareness, and the promotion of a multi-disciplinary approach to successful AMS programmes. Repeat PPS would be necessary to continually monitor the impact of interventions implemented. Broader participation is also encouraged to strengthen the evidence base.

Intestinal efflux transporters affect the gastrointestinal processing of many drugs but further data on their intestinal expression levels are required. Relative mRNA expression and relative and absolute protein expression data of transporters are commonly measured by real-time polymerase chain reaction (RT-PCR), Western blot and mass spectrometry-based targeted proteomics techniques. All of these methods, however, have their own strengths and limitations, and therefore, validation for optimized quantification methods is needed. As such, the identification of the most appropriate technique is necessary to effectively translate preclinical findings to first-in-human trials. In this study, the mRNA expression and protein levels of the efflux transporter P-glycoprotein (P-gp) in jejunal and ileal epithelia of 30 male and female human subjects, and the duodenal, jejunal, ileal and colonic tissues in 48 Wistar rats were quantified using RT-PCR, Western blot and liquid chromatography-tandem mass spectrometry (LC-MS/MS). A similar sex difference was observed in the expression of small intestinal P-gp in humans and Wistar rats where P-gp was higher in males than females with an increasing trend from the proximal to the distal parts in both species. A strong positive linear correlation was determined between the Western blot data and LC-MS/MS data in the small intestine of humans (R² = 0.85). Conflicting results, however, were shown in rat small intestinal and colonic P-gp expression between the techniques (R² = 0.29 and 0.05, respectively). In RT-PCR and Western blot, an internal reference protein is experimentally required; here, beta-actin was used which is innately variable along the intestinal tract. Quantification via LC-MS/MS can provide data on P-gp expression without the need for an internal reference protein and consequently, can give higher confidence on the expression levels of P-gp along the intestinal tract. Overall, these findings highlight similar trends between the species and suggest that the Wistar rat is an appropriate preclinical animal model to predict the oral drug absorption of P-gp substrates in the human small intestine.

Chitosan nanoparticles have gained attention as drug delivery systems (DDS) in the medical field as they are both biodegradable and biocompatible with reported antimicrobial and anti-leishmanial activities. We investigated the application of chitosan nanoparticles as a DDS for the treatment of cutaneous leishmaniasis (CL) by preparing two types of chitosan nanoparticles: positively charged with tripolyphosphate sodium (TPP) and negatively charged with dextran sulphate. Amphotericin B (AmB) was incorporated into these nanoparticles. Both types of AmB-loaded nanoparticles demonstrated in vitro activity against Leishmania major intracellular amastigotes, with similar activity to unencapsulated AmB, but with a significant lower toxicity to KB-cells and red blood cells. In murine models of CL caused by L. major, intravenous administration of AmB-loaded chitosan-TPP nanoparticles (Size = 69 ± 8 nm, Zeta potential = 25.5 ± 1 mV, 5 mg/kg/for 10 days on alternate days) showed a significantly higher efficacy than AmBisome^® (10 mg/kg/for 10 days on alternate days) in terms of reduction of lesion size and parasite load (measured by both bioluminescence and qPCR). Poor drug permeation into and through mouse skin, using Franz diffusion cells, showed that AmB-loaded chitosan nanoparticles are not appropriate candidates for topical treatment of CL.

Poly(lactic-co-glycolic acid) (PLGA) is a widely used biodegradable polymer in drug delivery and nanoparticle (NP) formulation due to its controlled drug release properties and safety profiles. Among the methods available for NP production, nanoprecipitation is distinguished by its simplicity and scalability. However, it requires careful optimisation to achieve the desired NP characteristics, making the process potentially lengthy and costly. This study aimed to assess and compare the predictive performance of Design of Experiments (DOE) and Machine Learning (ML) models for the optimisation of PLGA nanoparticle size and zeta potential produced by nanoprecipitation. Various ML methods were employed to predict particle size, with Extreme Gradient Boosting (XGBoost) identified as the best performing. The key finding is that integrating ML with DOE provides deeper insights into the dataset than either method alone. While ML outperformed DOE in predictive performance, as evidenced by lower root mean squared error values and higher coefficients of determination, both methods struggled to accurately predict zeta potential, generating models with high errors. However, ML proved more effective in identifying the parameters that most significantly influence NP size, even with a smaller DOE dataset. Combining DOE datasets with ML for parameter importance was particularly advantageous in situations where data is limited, offering superior predictive power and the potential to streamline experimental design and optimisation. These results suggest that the synergistic use of ML and DOE can lead to more robust feature analysis and improved optimisation outcomes, particularly for NP size. This integrated approach can enhance the accuracy of predictions and supports more efficient experimental design, streamlining nanoparticle production processes, especially under resource-constrained conditions.

We hypothesised that Hansen Solubility Parameters (HSPs) can be used to predict drug-nail affinities. Our aims were to: (i) determine the HSPs (δD, δP, δH) of the nail plate, the hoof membrane (a model for the nail plate), and of the drugs terbinafine HCl, amorolfine HCl, ciclopirox olamine and efinaconazole, by measuring their swelling/solubility in organic liquids, (ii) predict nail-drug interactions by comparing drug and nail HSPs, and (iii) evaluate the accuracy of these predictions using literature reports of experimentally-determined affinities of these drugs for keratin, the main constituent of the nail plate and hoof. Many solvents caused no change in the mass of nail plates, a few solvents deswelled the nail, while others swelled the nail to varying extents. Fingernail and toenail HSPs were almost the same, while hoof HSPs were similar, except for a slightly lower δP. High nail-terbinafine HCl, nail-amorolfine HCl and nail-ciclopirox olamine affinities, and low nail-efinaconazole affinities were then predicted, and found to accurately match experimental reports of these drugs' affinities to keratin. We therefore propose that drug and nail Hansen Solubility Parameters may be used to predict drug-nail interactions, and that these results can assist in the design of drugs for the treatment of nail diseases, such as onychomycosis and psoriasis. To our knowledge, this is the first report of the application of HSPs in ungual research.