Prediction of a CLDN18.2 Targeted Antibody Drug Conjugate Pharmacokinetics in Cancer Patients Using PBPK Modeling and Simulation.

Antibody-drug conjugates (ADCs) represent a promising anticancer approach. Although physiologically based pharmacokinetics (PBPK) modeling became essential in Pharmacometrics to characterize exposure in different tissues, very few PBPK models have been published for ADCs, none within the PK-Sim/MoBi software. To capture the pharmacokinetics (PK) of an anti-Claudin 18.2 ADC, a PBPK model was built in PK-Sim and MoBi and compared to observations from three clinical studies after intravenous (IV) administration in 109 patients with cancer. The PK parameters were considered inaccurate if the predicted error ratios were outside the two-fold error range (0.5-2). In PK-Sim, we defined one PBPK model comprising three compounds (ADC, payload, and naked antibody), which were mechanistically linked. This model captured the ADC PK profile. However, additional clearance mechanisms were essential to improve the fit of the ADC elimination phase. After integration of target-mediated drug disposition (TMDD) and deconjugation of the payload in MoBi, 3 parameters were optimized for each of the ADC and the payload (degradation rate constant and reference concentration of the target, deconjugation rate constant, lipophilicity, nonspecific hepatic clearance rate constant and passive renal clearance of the payload). The PK data were adequately captured for both observed compounds, with a predicted error ratio within the two-fold range: C_max__ADC (1.07-1.50), C_{max_Payload} (0.56-1.18), AUC_{0-504h_ADC} (0.73-1.23) and AUC_{0-504h_payload} (0.77-1.37). The "parameter optimization" of different parameters allowed accurately capturing the observed data for both ADC and payload in cancer patients for an anti-Claudin 18.2 ADC. This analysis paves the way for PBPK modeling of other ADCs currently in development.