Abstract
Despite current advances in clinical trial design and analysis, examples of drug failures after lengthy and expensive development processes can still be found. Clinical trials simulation (CTS) has been helpful in minimising the attrition factor of drug discovery and its capabilities continue to expand. In integrating different methodologies — pharmacokinetic and pharmacodynamic, statistical and computational — CTS has the potential to greatly aid the setup and execution of actual confirmatory clinical trials so that they have a much smaller risk of failure. However, caution is needed as something unknown cannot just be simply simulated from what is previously known. Much like an impossibility, akin to the idea of ‘bootstrapping’, the challenge is to look for answers to questions that nobody has posed before. Putting the focus on emulating real prospective patients, their full biological characteristics must be considered so that elusive adverse reactions that can stop drug development at a late clinical stage are avoided. The new generation of CTS may address some of the outstanding problems: experimental designs including hierarchical Bayesian approaches have been tried; Markov chain modelling and simulated annealing provide interesting features; machine learning classifiers, from genetic algorithms to artificial neural networks, may provide valuable insights; and game theory merged with CTS may produce significant advancements.
Published Version
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