Application and Development of Computational Approaches to Optimize Treatment of Malignancies using Routine Clinical Data

Abstract

The use of personalised medicine in oncology is gaining recognition as a way of enabling individualised tailored-treatment based on patient’s genetic signatures and clinical characteristics. The characterisation of drug exposure and the way it affects to the dynamics of the underlying disease under study (either through tumour size changes or biomarker dynamics), is key to support individualised disease monitoring and therapeutic strategies. The field of pharmacometrics is a potentially useful discipline which focuses on obtaining quantitative mathematical and statistical models of the different physiological processes from drug administration to measurement of drug exposure, disease dynamics (tumour size, biomarker response), and ultimately clinical outcome. In this thesis we will present examples of the use of different ways of characterising disease dynamics and pharmacometric tools to facilitate personalised medicine. These examples include applications to enable individualised medicine in routine clinical practice and to support model-based drug development.