Close
We aim to characterise the relationship between drug concentrations and effects. One uses these so called pharmacokinetic-pharmacodynamic relationships to select and optimise dosing regimens.
Research activities
Our research portfolio, that aims to improve our understanding of mechanisms underlying responses to medicines, currently has three research themes:
- There are currently no good biomarkers that can predict the tuberculosis treatment outcome during the first eight weeks of treatment. Clinical trials investigating novel anti-tuberculosis drugs and standard of care are consequently very long given that treatment of tuberculosis currently lasts for six months. To that end we have designed and been running observational pharmacokinetic-pharmacodynamic cohorts in the UK and abroad to investigate in-vivo candidate biomarkers for cure during treatment of active tuberculosis. (link1 and link2).
- Biomarkers for bacterial sub-populations that are able to survive antimicrobial combination treatment are crucial for rational based antimicrobial combination therapy selection. However, biomarkers are lacking, limiting our ability to investigate responses to anti-mycobacterial drug-drug combinations. To that purpose we have setup a series of in-vitro experiments at the UCL Centre for Clinical Microbiology to investigate mechanisms underlying emergence of resistance. (link3 & link4)
- A single big pharmacokinetic-pharmacodynamic data source is currently lacking, which prevents taking full benefit from machine learning within the space of pharmacokinetic-pharmacodynamic modelling methods. We therefore started PKPDai, an initiative that brings together quantitative pharmacometric knowledge into a single open-source platform, thereby making it amenable to the application of machine learning. (link5 & link6)
Research disciplines, methods & techniques
Pharmacology
Microbiology
In-vivo pharmacokinetic-pharmacodynamic studies
In-vitro microbiology
In-vitro pharmacology
In-vitro Hollow Fibre Infection model (HFIM)
Population Pharmacokinetic-Pharmacodynamic modelling (pop PKPD)
Natural Language Processing (NLP)
Software and Databases
Pump settings for in-vitro hollow-fibre experiments