Every patient is different - his/her genomes, environment, disease history and exposure to drugs. Tumours, in particular, are often heterogeneous in their genetic make-up and their response to drugs, both within and between samples. Classic clinical trials basically ignore this complexity or, as in stratified medicine, attempt to reduce it to an analysis of a small number of still enormously heterogeneous patient groups. Medicine, however, is not the only area in which we are faced with such complex ‘n = 1' (every individual case is different) situations. The weather we experience today, characterised by tens of terabytes of measurement data, has never occurred before and will never occur again. Similar to the situation in medicine, we cannot predict the development of today's weather by looking for identical weather conditions in the past, and we cannot, in real life, test drugs for every individual patient in clinical trials with large numbers of biologically identical patient replicas. We can, however, do it with the help of models duplicating the ‘n = 1' situation on the computer, an approach which will also have to be used in both patient treatment and prevention as well as drug development in the future if we do not want to continue to make dangerous and expensive mistakes in real life.

Keywords:
Drug discovery, Drug development, Predictive modelling, In silico clinical trials, Virtual patient, Mechanistic models, Personalised medicine
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2015
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