Pharmacometric analysis (modelling and simulation) is a tool to translate basic and clinical research into improved pharmacotherapetic use. Pharmacometrics is the science which deals with the quantitative description of disease, drug effects and variability. The rapidly expanding area of pharmacogenomics is also providing a constant source of genetic differences between people which have major impacts on drug effects and pharmacokinetics. Pharmacometric analyses quantify drug, disease and trial information to aid efficient drug use, development, and regulatory decisions. This translational research is achieved through developing mathematical models which integrate knowledge from prior understanding, related compounds and biology, together with the ability to include both richly sampled data and more limited/incomplete data typically unusable in traditional statistical approaches. Once a quantitative model has been developed it can then be used for a variety of purposes, from predicting or simulating outcomes of previously untested dose regimens of drugs or treatments, optimizing future trial designs, identifying ways treatment can be optimized in special patient populations, or even truly individualized medicine through Bayesian forecasting.
In the past, pharmacometrics has been poorly understood by mainstream biomedical researchers. However, this is now changing and increasingly researchers, regulatory authorities and funding bodies are recognizing the power that pharmacometric analyses provide. For example, many of the dose recommendations in medicine product information leaflets used by clinicians are the result of pharmacometirc analyses, and many drug development decisions are pharmacometrics-informed.
This group has been successful in applying pharmacometric analyses in areas such a chemotherapeutics, opioid analgesic drugs, rheumatoid arthritis, and the absorption of orally administered drugs. We are also skilled in developing web apps for the communication of models to end-users and dashboard systems for interpretation of the results and the presentation / understanding of data more generally.
On-going collaborations with various research teams both internal and external to UniSA, as well as partners in the pharmaceutical industry, will offer students interested in translational research opportunities to work on multidisciplinary end-user targeted research projects that will have a major impact on rational use of medications, patient outcomes, and even medicine development.
Researchers
Current research projects
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Quantitative understanding of oral drug delivery and absorption
Associate Professor David Foster
Dr Ahmad AbuhelwaDeveloping quantitative prediction models for the effect of food, gastrointestinal conditions, and formulation characteristics on the in vivo exposure of orally-administered drugs and help provide strategies to develop/optimize novel drug products and support commercialization of drug products with regulatory authorities. As mentioned in UniSA’s press release , Mayne Pharma won US Food and Drug Administration (FDA) approval for Tolsura™, a new patented capsule formulation of itraconazole, which is an antifungal drug treatment for intractable and systemic fungal infections in adult patients with life-threatening conditions such as cancer and AIDS. And much of the detailed modelling of the drug was done right here at UniSA’s Australian Centre for Precision Health. Mayne Pharma’s Vice President of Scientific Affairs, Dr Stuart Mudge says working together with research staff at UniSA and with other science collaborators on the project has been invaluable.
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Quantitative description of the cardiovascular system
Associate Professor David Foster
Professor Richard UptonThe cardiovascular system is complex, and changes to one part of the system (eg contractility of the heart) affecting the whole system (eg blood pressure). This results in homeostatic feedback loops causing changes to other parts of the system (eg heart rate). As a result, traditional analyses treating cardiovascular measurements as independent factors can lead to less than optimal conclusions about the impact of treatments, the understanding of how a medicine "works" in the whole body, the best way use a medicine, or how to design a trials to do so. Variability across the population in the response to drugs, and in the responsiveness of the cardiovascular system between people, leads to a range of outcomes even after the same dose of a drug. In the development stage medicines are initially tested in animal models, and the complex interrelationships within the cardiovascular system makes prediction into humans problematic. This project aims to further refine a population model of the cardiovascular system we have developed, by using animal and human data to quantitatively describe the cardiovascular system. It will facilitate a deeper understanding of the variability in response to cardiovascular medicines, facilitate future clinical trial design and interpretation of the results, and assist with drug development.
