The development of medicines is a cost and resource intensive endeavour and the fear of failure, especially in the latter stages of a project can often result in researchers pursuing only conservative strategies, which limit innovation.
The use of computational modelling to predict the likely outcomes of an experiment can help to guide the rational design and optimisation of medicine formulations, medical devices and drug delivery systems, which can help to manage risk and reduce the costs associated with medicines development programmes.
Using a number of different software, the group is able to identify the parameters that are crucial to the optimisation of a medicine formulation, medical device or drug delivery system and identify the minimum number of experiments that should be conducted to explore the impact of each of the parameters.
By using high-quality experimental datasets obtained from data-mining and in-house experiments the group are able to produce robust and predictive models for a range of physical and chemical properties (such as the extent to which a molecule will dissolve in water, or be absorbed through the skin), which can be used to inform and influence the direction of medicines development projects.
By exploiting biological datasets, the group is able to produce predictive models that identify the likely toxicological properties of a medicine This includes how medicines in the body will be metabolised, what likely products of metabolism will be, and whether or not they will be toxic. Accurate models of this nature can help to guide toxicological studies and are instrumental in reducing, refining and replacing the use of animal models in toxicological studies.