Pharmacokinetic Modeling and Predictive Performance: Practical Considerations for Therapeutic Monoclonal Antibodies

Abstract

Population pharmacokinetic (PopPK) model parameter estimation and predictive performance depend on the data adequacy for model building. PopPK models of therapeutic monoclonal antibodies (mAbs) may not be well supported by commonly used sparse sampling in late-stage development because of the slow absorption (days) and long half-life (weeks) of mAbs, affecting accuracy of predicted exposure metrics which are often used to support drug development. A case study was presented for a representative mAb to compare the predictive performance of two established PopPK models from their respective data. Differences in datasets for model building (including sample size, sampling schedule and route of administration), model structure and parameters, and key derived exposure metrics were compared, and the resulting differences in model prediction were elaborated. With the majority of the data used for developing models being trough concentration (C_trough) data, both models projected similar C_trough and area under the concentration-time curve (AUC) but different peak concentrations (C_max) at steady state following the same subcutaneous dose regimen. Our case study supports the importance of appropriate sampling schemes for PopPK model development and exposure metric estimation. We recommend collecting proper random pharmacokinetic samples, in addition to troughs, to allow adequate characterization of PopPK models for mAbs. Selecting the informative model and relevant pharmacokinetic metrics could be critical in driving drug development decision-making, especially in simulation-based exposure matching to inform doses in special populations such as pediatrics.