Optimized scaling of translational factors in oncology: from xenografts to RECIST

Purpose Tumor growth inhibition (TGI) models are regularly used to quantify the PK-PD relationship between drug concentration and in vivo efficacy in oncology. These models are typically calibrated with data from xenograft mice and before being used for clinical predictions, translational methods have to be applied. Currently, such methods are commonly based on replacing model components or scaling of model parameters. However, difficulties remain in how to accurately account for inter-species differences. Therefore, more research must be done before xenograft data can fully be utilized to predict clinical response. Method To contribute to this research, we have calibrated TGI models to xenograft data for three drug combinations using the nonlinear mixed effects framework. The models were translated by replacing mice exposure with human exposure and used to make predictions of clinical response. Furthermore, in search of a better way of translating these models, we estimated an optimal way of scaling model parameters given the available clinical data. Results The predictions were compared with clinical data and we found that clinical efficacy was overestimated. The estimated optimal scaling factors were similar to a standard allometric scaling exponent of - 0.25. Conclusions We believe that given more data, our methodology could contribute to increasing the translational capabilities of TGI models. More specifically, an appropriate translational method could be developed for drugs with the same mechanism of action, which would allow for all preclinical data to be leveraged for new drugs of the same class. This would ensure that fewer clinically inefficacious drugs are tested in clinical trials.

Automated summary

Tumor growth inhibition (TGI) models are important for quantifying the PK-PD relationship between drug concentration and efficacy in oncology. However, accurately accounting for inter-species differences remains a challenge, and more research is needed before xenograft data can be fully utilized for clinical predictions. In this study, TGI models were calibrated to xenograft data and translated to human exposure for clinical response predictions. Results showed that clinical efficacy was overestimated and optimal scaling factors were similar to a standard scaling exponent of -0.25. This research has potential to enhance the translational capabilities of TGI models.