Some aspects of the statistical design and analysis of the Comet (single cell gel electrophoresis) assay have been evaluated by means of a simulation study. The tail length and tail moment were selected for the quantification of DNA migration. Results from the simulation study showed that the choice of measure to summarize the cells on each slide is extremely important in order to facilitate an efficient analysis. For tail moment, the mean of log transformed data is clearly superior to the other evaluated measures, whereas using the mean of raw data without transformation can lead to very inefficient analyses. The 90th percentile, capturing the upper tail of the distribution, performs well for the tail length, with a slight improvement obtained by applying a log transformation prior to calculations. Furthermore, the simulation study has been used to assess the appropriateness of some models for statistical analysis and to address the issue of design (i.e. number of cultures or animals in each group, number of slides per animal/culture and number of cells scored per slide). Combining the results from the simulations with practical experience from the pharmaceutical industry, we conclude the paper by providing concise recommendations regarding the design and statistical analysis in the Comet assay.

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The development of a new drug is an extremely high-risk enterprise. The attrition rates of development projects and the average costs for each launched product are daunting, and the completion of a development program requires a very long time horizon. These facts imply that there are huge potential gains, should one be able to improve efficiency and enhance decision-making capabilities. In this paper, we argue that substantial gains can be achieved by adapting a holistic view of drug development. Historically, too much planning, design and decision-making in the pharmaceutical development has been based on locally optimising separate parts of the development program, and too often important sources of uncertainty are ignored. We propose instead a model-based approach built on two essential pillars; (1) an integrated holistic view of the development program, including post-launch marketing and sales, with all parts evaluated simultaneously; (2) an explicit appreciation of all relevant sources of uncertainty. Computer simulations are utilised to evaluate the properties of the program options at hand, and to provide valuable quantitative decision support. Applications of this modelling approach have proven to add large value to development projects in terms of better program options being generated and more value-adding decisions taken.