Predicting animal to human translation: A proof of concept study using qualitative comparative analysis

Drug development suffers from high attrition rates; promising drug candidates fail in clinical trials. Low animal-to-human translation may impact attrition. We previously summarised published translational success rates, which varied from 0% to 100%. Based on analyses of individual factors, we could not predict translational success. Several approaches exist to analyse effects of combinations of potential predictors on an outcome. In biomedical research, regression analysis (RGA) is common. However, with RGA it is challenging to analyse multiple interactions and specific configurations ({approx} combinations) of variables, which could be highly relevant to translation. Qualitative comparative analysis (QCA) is an approach based on set theory and Boolean algebra. It was successfully used to identify specific configurations of factors predicting an outcome in other fields. We reanalysed the data from our preceding review with a QCA. This QCA resulted in the following formula for successful translation: O_FD O_INLINEFIG[Formula 1]C_INLINEFIGM_FD(1)C_FD Which means that within the analysed dataset, the combination of relative recency ([~] means not; >1999), analyses at event or study level (not at intervention level), n<75, inclusion of more than one species and quantitative (instead of binary) analyses always resulted in successful translation (>85%). Other combinations of factors showed less consistent or negative results. An RGA on the same data did not identify any of the included variables as significant contributors. While these data were not collected with the QCA in mind, they illustrate that the approach is viable and relevant for this research field. The QCA seems a highly promising approach to furthering our knowledge on animal-to-human translation and decreasing attrition rates in drug development.