Model-informed drug development (MIDD) approaches have advanced rapidly in drug development in recent years. Additionally, the Prescription Drug User Fee Act (PDUFA) VI has specific commitments to further enhance MIDD. Tumor growth dynamic (TGD) modeling, as one of the commonly utilized MIDD approaches in oncology, fulfills the purposes to accelerate the drug development, to support new drug and biologics license applications, and to guide the market access. TGD modeling’s scope includes the assessment of tumor growth inhibition/tumor shrinkage and tumor re-growth, and their potential as surrogates of predicting the survival probability with the cancer treatment.

The TGD approach has been utilized across various drug development stages. In pre-clinical and translational stages, it was used to select promising drug candidates, to assist the pharmacological projection of the starting human dose in first-in-human trials, and to leverage clinical cancer patient data. TGD models have also been applied in early and late stages of clinical development. During the phase 1/II stage, TGD can support early clinical decisions (i.e., “Go/No-go” decisions for moving to phase III), via predicting survival outcomes using the longitudinal tumor size data as the surrogate metrics. Additionally, TGD can also address the medical concerns in clinical practice, such as the impact of clinical covariates by quantifying the effects of genotype variations on patient r esponse. In addition, TGD models have been applied to identify the sub-patient populations who would benefit the most for an intended treatment. Further, models based on tumor growth data obtained from patients can help in deciding on the optimal dose and dosing algorithms for individual patients during market access.