Quantification of uncertainty within systems pharmacology to optimise personalised therapy

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The proposed research will demonstrate how the quantification of uncertainty within theoretical pharmacology models can be used to optimise personalised therapy. The current one-dose-fits-all viewpoint, dosing for the 'average' human and empirical derivations of dosing regimens are clearly not sufficient to effectively treat a diverse population. Personalised medicine, with the determination of treatment plans influenced by genetic factors and historical cases of toxicity, represents a step towards improving clinical outcomes. However, when an individual is deemed not suitable for treatment with the standard medicine/dose based on pharmacogenetics, an alternative must be found which is likely to either be costlier, less effective or potentially toxic. This scenario can be improved by having a greater understanding of both the mechanisms of toxicity in subpopulations and a proper quantification of risk and efficacy. Mathematical and statistical tools can help to improve the understanding of the mechanisms of toxicity, optimise treatment (e.g. alternative dosing regimens, multidrug administration, assess alternative medicine strategies) and communicate the risk of suggested therapy based on uncertainty in the model simulations. To develop and test this proposed quantitative framework during the fellowship, two exemplar drugs will be studied based on adverse reactions in anticoagulation therapy. Warfarin will be used to develop and validate the framework, while the newer direct anticoagulants (e.g. dabigatran, rivaroxaban), whose metabolism and toxicity pathways are less well characterised, will be used for more predictive research. Statistical techniques will be applied to define and quantify uncertainties within model output and define risk when translating to optimised treatment strategies incorporating different genetic and non-genetic factors associated with variability in drug-induced adverse reactions. The integration of applied mathematics and statistics comprises a potentially exciting new field bringing two modelling approaches together to better quantify uncertainty and inform industrial drug development and therapy.