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.

Background: Accurately determining pediatric dosing is essential prior to initiating clinical trials or administering medications in routine clinical settings. In children, ethical considerations demand careful evaluation of both safety and effectiveness. Typically, dosing recommendations for therapeutic proteins, such as monoclonal antibodies (mAbs), are derived from adult dosages using body weight as a scaling factor. However, this method overlooks key physiological and biochemical distinctions between pediatric and adult patients. Therefore, this could lead to the underexposure of mAbs, limiting their efficacy in this population. Additional methods are necessary to predict pediatric doses mechanistically. For small molecules, physiologically based pharmacokinetic (PBPK) models have been extensively used to predict pediatric doses based on physiological age-related changes and enzymes/transporters ontogeny. This study aims to evaluate the ability of PBPK models to predict mAbs’ pediatric exposure. Methods: Three mAbs were used for model development and validation: bevacizumab, infliximab, and atezolizumab. The PBPK models were built using GastroPlus^© Biologics module. For each mAb, the PBPK model was developed based on observed data in healthy and/or patient adults. Then, the physiological parameters were scaled to describe the pediatric physiology to predict exposure to the pediatric populations. Predicted plasma concentration–time courses were overlaid with reported observed data to assess the ability of the PBPK model to predict pediatric exposure. Results: Results showed that PBPK models accurately predicted pediatric pharmacokinetics for mAbs. Conclusions: This research marks a significant step in validating mechanistic extrapolation methods for biologics exposure prediction in children using PBPK models.