Preclinical, clinical, phase I – IV+

Pharmacometrics measures drug effects, disease and variability. It is highly valuable to drug development and therapeutic and regulatory decision-makers because it can identify and determine differences between in vitro and in vivo data.

Pharmacometrics uses drug models to describe the relationship between exposure to medicines (pharmacokinetics) and changes in medicines from exposure (pharmacodynamics). It also uses disease models to describe the progression of disease over time, placebo effects and the relationship between biomarkers and clinical outcomes.

Population pharmacometrics makes better dosing and clinical outcomes possible. It can estimate population and individual parameters simultaneously, and quantify variability due to differences between individuals or random inputs. It can also investigate and incorporate the effects of secondary variables, such as body size, age or disease severity on model parameters, including drug clearance from the body, to account for some of the differences between individuals.

CMUS collaborates extensively with our colleagues in the Monash Institute of Pharmaceutical Sciences to advance preclinical and translational drug development programs. Our research focuses on a broad range of drug therapies and disease areas. We have particular expertise in using specialised software for population pharmacometric data analysis.

Key research interests

  • population pharmacokinetic and pharmacodynamic modelling of:
    • antimicrobial agents
    • antiviral compounds
    • cancer
    • cystic fibrosis
    • cardio-endocrine hormones
    • diabetes
    • malaria
    • dosing in obesity
    • body fluid and tissue penetration (including bone, breast milk, lung/epithelial lining fluid)
  • optimisation of dosing in patients with renal dysfunction
  • Bayesian optimisation of pharmacotherapy (using TCIWorks target concentration intervention software)
  • translational modelling to quantify the progression from drug discovery to preclinical and clinical drug development
  • mechanism-based modelling for anti-infective drug discovery, preclinical and clinical development and optimal patient therapy
  • optimal design of clinical trials and preclinical experimental programs
  • quality use of medicines

Principal investigator