ADCs form a therapeutic class that has demonstrated immense potential for transformative clinical responses in several types of cancers. Due to their complexity, however, predicting clinical properties remains a challenge. The mAb, linker, and payload all need to be optimized for a particular tumor target, indication, and patient population.. More importantly, traditional approaches can be misleading when considered in isolation, and several notable clinical ADC failures have been reported in recent years. Here we present a next generation multiscale QSP model calibrated to T-DM1 and T-DXd that can be used to predict clinical efficacy, thereby facilitating ADC design, lead candidate selection, and clinical dosing schedule optimization.
Conclusions & Future Directions
We have developed a multiscale mechanistic model describing a generic ADC and calibrated it to published data for two HER2-targeting clinical ADCs. The model was able to predict differences in tumor payload accumulation that agree with observed differences in clinical outcomes. Virtual clinical trials conducted using the model were able to describe published data, and highlighted the importance of HER2 expression on response to T-DM1 therapy. Future simulations will include virtual clinical trials for T-DXd and further analysis to understand the interplay between various ADC design choices and disease- and patient-specifc characteristics on a quantitative level. Alternative dosing schedules and levels will be explored to identify if superior efcacy or therapeutic index regimes exist. Finally, the model will be expanded to describe additional ADC and disease combinations.