• Optimized dosing regimens for the combinations of sulphonamides and trimethoprim in veterinary medicine (SulTAn)

    To fill the gaps related to the pharmacokinetics of Trimethoprim and Sulphonamides, and to the pharmacodynamics (PD) of their interaction on veterinary pathogens across multiple animal species.

    This project will focus on the combination of different S with TMP in veterinary medicine and will aim at determining the needed adjustments or revisions to optimize TMPS dosage regimens in domestic animal species.


  • Cellular mechanisms of impaired neurodevelopment following early life exposure to air pollution

    UNICEF estimates that over 100 million infants worldwide are exposed to toxic air pollution. We are investigating how this alters vulnerable brain cell development during pregnancy and after birth. Air pollution is a serious common public health concern increasingly associated with morbidity and mortality and resulting in an estimated 7 million premature deaths per year. Air pollution is a mixture of several components, including particulate matter (PM) derived from traffic, fuel burning and industry. The World Health Organisation identified that over 90% of the population are exposed to levels of PM2.5 that are significantly higher than recommended levels.


  • PK/PD informed clinical breakpoint determination for colistin in chicken to limit emergence of resistance and improve One Health antimicrobial sustainability

    This project aims to evaluate the impact of colistin use on antimicrobial resistance and rationalise dosing through a combination of in vivo pharmacokinetic (PK) dose studies, in vitro pharmacodynamic (PD) and antimicrobial susceptibility testing, and advanced in silico PK/PD modelling.

    To maintain colistin as an essential antimicrobial for both human and veterinary use, recent and reliable data regarding the pharmacokinetics (PK) and pharmacodynamics (PD) at the clinical dose in poultry, and its impact on the potential selection of resistance is required to inform application and policy.


  • Machine learning algorithms for predicting drug resistance against tuberculosis in people

    Tuberculosis disease (TB), caused by Mycobacterium tuberculosis, is an important global public health issue, and its drug resistance, caused by genetic mutations in the M. tuberculosis genome, poses serious challenges for effective control. Current molecular diagnostic tests are imperfect as they do not target all resistance mechanisms and drugs, nor do they inform on transmission clusters, and are therefore unable to guide completely effective individualised therapy.


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