In a recent paper in Nature Communications, Narayan et al described the development of an in silico method to predict synergy of drug combinations, validated in vivo using 5 different models for 5 different drug pairs. The published analysis of the in vivo experimental data used the Chou and Talalay Combination Index, which ignores important aspects of the study design, including, but not limited to the variability between individual mice and the longitudinal nature of the data. Furthermore, the original publication reported the Combination Index as static numbers without properly accounting for experimental variability. When 95% confidence intervals are recalculated using bootstrapping methods, 4 out of 5 drug pairs do not show a statistically significant synergistic effect. Three models (originally figures 5A, B and E) are subject to severe inaccuracies in the data handling and reporting. In one model, (originally figure 5D), the data better supports an additive effect between drugs rather than synergy. As the Chou and Talalay Combination Index is poorly suited to this data, we applied mixed-effects models fitted to the longitudinal tumor growth data as an alternative means of estimating synergy. The results support the findings from the bootstrapping analysis, namely that the majority of the drug pairs do not achieve a synergistic effect. We conclude that the in vivo validation of the in silico method to predict synergy of drug combinations as proposed by Narayan et al has a negative outcome when analyzed with appropriate statistical methods. ### Competing Interest Statement OvT: None RM was recently recruited as group leader of the Biostatistics Centre of the Netherlands Cancer Institute. Before this she was a biostatistician at the VUmc, which is the institute of Narayan and colleagues. RM is a coauthor of the paper under discussion, but she was not involved in the analyses of the in vivo data.
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