Letter to the editor: Quality criteria for computational models predicting individual outcomes in CAR-T cell therapy

[...]using statistical summary parameters instead of individual-level data will dilute signals, create artificial, never observed data, and be overoptimistic in the prediction of individual trajectories. [...]relative standard errors or confidence intervals, which quantify the uncertainty in the parameter estimates, are not provided. The practical usefulness of most of the proposed factors, however, is low as no link is made between the prediction factors and patient or treatment characteristics. [...]should one want to use the developed computational model to make an early response prediction for a new patient by calculating the CAR-T cell function or negative relapse factor, this would require fitting the model to the patient’s individual cell kinetic data. While certainly being accurate, the novelty of this factor is low, as the positive correlation between CAR-T cell exposure and beneficial outcome has been well established previously. [...]supported by an independent clinical analysis,5 we have identified a clinical composite score of CAR-T cell peak expansion normalized to the baseline tumor burden to be a better predictor for outcome than expansion alone.4 In general, while we are pleased that more computational research is being applied to the field of cell therapy, further development of any computational model (including our own) will need to include rigorous (re-)examination of modeling strategies, proper validation, as well as judicious and critical discussion of findings and their limitations before any of the developed models or model-derived prediction factors can be considered for use beyond research purposes.