ABSTRACT Cachexia is a metabolic condition that accelerates the clearance of monoclonal antibodies in cancer patients and is a known mechanism causing time‐dependent clearance. Successful anticancer treatment often ameliorates symptoms of cachexia, reducing the drug clearance over time especially in patients who respond. Serum albumin level is a common biomarker of cachexia that is frequently associated with antibody drug clearance. Physiologically based pharmacokinetic (PBPK) models of antibody drugs have incorporated albumin metabolism but have not been applied to describe time‐varying clearance due to improvement in cachexia. The objective of this analysis was to evaluate albumin levels as a biomarker that is predictive of changes in antibody clearance due to cachexia. A PBPK model that jointly describes metabolism of albumin and biologic drugs was fitted to longitudinal albumin data from cancer patients treated with durvalumab and was used to predict changes in durvalumab clearance over time. PBPK model predictions were compared to empirical population pharmacokinetic (PK) models of durvalumab and other checkpoint inhibitors fitted directly to clinical PK. The model fitted the observed albumin data in cancer patients closely, and the three fitted parameters showed low uncertainty (RSE < 10%). By accounting for longitudinal albumin data in patients, the PBPK model recapitulated the observed magnitude of the change in clearance of durvalumab without fitting to clinical PK data. The model simulation demonstrated that utilization of albumin levels as a marker of cachexia in PBPK models can be used to mechanistically predict time‐dependent clearance of monoclonal antibodies.
Proctor et al. (Fri,) studied this question.