Decision-making in clinical drug development.

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The most important type of experiment in clinical trials is the randomised clinical trial. From synthesising to testing a novel treatment pharmaceutical companies put a large amount of money at risk which makes it evident that they are eager to optimise their decisions in order to maximise their revenue. A current major problem is that although there is a surge of knowledge about the molecular basis of diseases, a significant proportion of novel treatments still fail. More importantly, the failure rate of Phase III trials is high and this is problematic and unintuitive as usually only promising treatments move to Phase III and they are the most expensive to conduct. It is evident that failure rates as high as the current ones are not sustainable. The aim of this project is to use algorithmic frameworks and data-driven approaches to decision-making in clinical trials, with an ultimate goal of guiding in decision-making of lung cancer clinical trials. Our focus is on finding the optimal decisions for the combination of Phase II and Phase III in any scenario (e.g., a case of a rare disease). This will be done by creating mathematical models that are easy to use by clinicians, are interpretable and are solved efficiently. This will, in turn, be used by pharmaceutical companies to make formal decisions and thus maximise future benefit while minimising errors.