An integrated multiple-analyte pharmacokinetic model to characterize trastuzumab emtansine (T-DM1) clearance pathways and to evaluate reduced pharmacokinetic sampling in patients with HER2-positive metastatic breast cancer.

BACKGROUND AND
OBJECTIVE: Trastuzumab emtansine (T-DM1) is an antibody-drug conjugate recently approved by the US Food and Drug Administration for the treatment of human epidermal growth factor receptor 2 (HER2)-positive metastatic breast cancer previously treated with trastuzumab and taxane chemotherapy. It comprises the microtubule inhibitory cytotoxic agent DM1 conjugated to the HER2-targeted humanized monoclonal antibody trastuzumab via a stable linker. To characterize the pharmacokinetics of T-DM1 in patients with metastatic breast cancer, concentrations of multiple analytes were quantified, including serum concentrations of T-DM1 conjugate and total trastuzumab (the sum of conjugated and unconjugated trastuzumab), as well as plasma concentrations of DM1. The clearance of T-DM1 conjugate is approximately 2 to 3 times faster than its parent antibody, trastuzumab. However, the clearance pathways accounting for this faster clearance rate are unclear. An integrated population pharmacokinetic model that simultaneously fits the pharmacokinetics of T-DM1 conjugate and total trastuzumab can help to elucidate the clearance pathways of T-DM1. The model can also be used to predict total trastuzumab pharmacokinetic profiles based on T-DM1 conjugate pharmacokinetic data and sparse total trastuzumab pharmacokinetic data, thereby reducing the frequency of pharmacokinetic sampling.
METHODS: T-DM1 conjugate and total trastuzumab serum concentration data, including baseline trastuzumab concentrations prior to T-DM1 treatment, from phase I and II studies were used to develop this integrated population pharmacokinetic model. Based on a hypothetical T-DM1 catabolism scheme, two-compartment models for T-DM1 conjugate and trastuzumab were integrated by assuming a one-step deconjugation clearance from T-DM1 conjugate to trastuzumab. The ability of the model to predict the total trastuzumab pharmacokinetic profile based on T-DM1 conjugate pharmacokinetics and various sampling schemes of total trastuzumab pharmacokinetics was assessed to evaluate total trastuzumab sampling schemes.
RESULTS: The final model reflects a simplified catabolism scheme of T-DM1, suggesting that T-DM1 clearance pathways include both deconjugation and proteolytic degradation. The model fits T-DM1 conjugate and total trastuzumab pharmacokinetic data simultaneously. The deconjugation clearance of T-DM1 was estimated to be ~0.4 L/day. Proteolytic degradation clearances for T-DM1 and trastuzumab were similar (~0.3 L/day). This model accurately predicts total trastuzumab pharmacokinetic profiles based on T-DM1 conjugate pharmacokinetic data and sparse total trastuzumab pharmacokinetic data sampled at preinfusion and end of infusion in cycle 1, and in one additional steady state cycle.
CONCLUSIONS: This semi-mechanistic integrated model links T-DM1 conjugate and total trastuzumab pharmacokinetic data, and supports the inclusion of both proteolytic degradation and deconjugation as clearance pathways in the hypothetical T-DM1 catabolism scheme. The model attributes a faster T-DM1 conjugate clearance versus that of trastuzumab to the presence of a deconjugation process and suggests a similar proteolytic clearance of T-DM1 and trastuzumab. Based on the model and T-DM1 conjugate pharmacokinetic data, a sparse pharmacokinetic sampling scheme for total trastuzumab provides an entire pharmacokinetic profile with similar predictive accuracy to that of a dense pharmacokinetic sampling scheme.