Respiratory disorders, such as chronic obstructive pulmonary disease (COPD), have a high global burden of disease. Patients are predisposed to repeated bacterial lung infections, causing exacerbations and accelerating lung function decline. The emergence of antimicrobial resistance to existing antimicrobials, means infections are increasingly difficult to treat or have become ineffective entirely, highlighting an unmet clinical need for novel therapies. Animal models have been instructive in studying disease mechanisms and support pre-clinical efficacy evaluation of novel antimicrobials using biomarkers of inflammation, microbiology, and lung function. However, whilst these biomarkers (eg. FEV1 and inflammation) correlate well with human patients, they show a poorer correlation with overall clinical outcome impacting clinical translatability.
This project aims to develop more clinically translatable in vivo animal models by incorporating novel biomarkers to screen novel antimicrobials. We hypothesise these models would more sensitively evaluate drug effects and translate better to patient outcome. Our physiology team have developed a novel lung physiology biomarker called “Symmetric Project Attractor Reconstruction” (SPAR) that replots respiratory flow waveforms from resting breathing, quantifying their shape and variability. SPAR metrics more sensitively classified early lung function deterioration and inflammation, when compared to routine metrics, in animal models of respiratory disease also correlating with data from COPD patients, accessed through our NHS partners.
We are also developing novel compounds (including pleurocidin analogues) which have shown promising antimicrobial effects in vitro and in vivo. The new biomarkers will be integrated into clinically relevant respiratory infection models and used to screen both existing and novel antimicrobial agents. We predict a combination of biomarkers will more sensitively quantify lung-function deterioration following infection, and rate of recovery following treatment. We aim to identify signatures which we aim to cross validate in retrospective patient data. This is a pre-clinical drug development project aiming to advance these drugs into clinical trials.