Jeffrey R. Proctor

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.

Time‐dependent pharmacokinetics (TDPK) is a frequent confounding factor that misleads exposure‐response (ER) analysis of therapeutic antibodies, where a decline in clearance results in increased drug exposure over time in patients who respond to therapy, causing a false‐positive ER finding. The object of our simulation study was to explore the influence of clinical trial designs on the frequency of false‐positive ER findings. Two previously published population PK models representative of slow‐ (pembrolizumab) and fast‐onset (rituximab) TDPK were used to simulate virtual patient cohorts with time‐dependent clearance and the frequency of false‐positive ER findings. The impact of varying the number of dose groups, dose range, and sample size was evaluated over time. Study designs with a single tested dose level showed a high probability of showing a false‐positive ER finding. When TDPK has a slow onset, use of exposure measures from early timepoints in ER analysis significantly reduces the risk of a false‐positive, while with fast onset it did not. Randomization of patients to two dose levels greatly reduced the risk, with a threefold or greater dose range offering the greatest benefit. The likelihood of false‐positive increases with a larger sample size, where greater care should be taken to identify confounding factors. Clinical trial simulation supports that appropriate clinical study design and analysis with adequate dose exploration can reduce but cannot entirely eliminate the risk of misleading ER findings.

Exposure–response (ER) analysis is used to optimize dose and dose regimens during clinical development. Characterization of relationships between drug exposure and efficacy or safety outcomes can be utilized to make dose adjustments that improve patient response. Therapeutic antibodies typically show predictable pharmacokinetics (PK) but can exhibit clearance that decreases over time due to treatment. Moreover, time‐dependent changes in clearance are frequently associated with drug response, with larger decreases in clearance and increased exposure seen in patients who respond to treatment. This often confounds traditional ER analysis, as drug response influences exposure rather than the reverse. In this review, we survey published population PK analyses for reported time‐dependent drug clearance effects across 158 therapeutic antibodies approved or in regulatory review. We describe the mechanisms by which time‐dependent clearance can arise, and evaluate trends in frequency, magnitude, and time scale of changes in clearance with respect to indication, mechanistic interpretation of time‐dependence, and PK modeling techniques employed. We discuss the modeling and simulation strategies commonly used to characterize time‐dependent clearance, and examples where time‐dependent clearance has impeded ER analysis. A case study using population model simulation was explored to interrogate the impact of time‐dependent clearance on ER analysis and how it can lead to spurious conclusions. Overall, time‐dependent clearance arises frequently among therapeutic antibodies and has spurred erroneous conclusions in ER analysis. Appropriate PK modeling techniques aid in identifying and characterizing temporal shifts in exposure that may impede accurate ER assessment and successful dose optimization.

Purpose To characterize the pharmacokinetics (PK) of zanidatamab including evaluation of the impact of intrinsic and extrinsic patient factors. To investigate alternative dosing regimens to improve caregiver convenience and reduce zanidatamab wastage. Methods Serum zanidatamab concentrations were obtained from 305 patients with advanced or metastatic breast cancer, gastroesophageal adenocarcinoma (GEA), biliary tract cancer, and other HER2-expressing cancers from four ongoing phase I and II clinical trials. Zanidatamab PK were described using population methods. The exposure of alternative dosing regimens and the impact of dose delay was estimated by model simulation. Results A two-compartment model with parallel linear and nonlinear clearance from the central compartment adequately described zanidatamab PK. At the recommended dose regimens of 20 mg/kg Q2W and 30 mg/kg Q3W, zanidatamab clearance was primarily linear at steady state. At steady state, 30 mg/kg Q3W zanidatamab returns within 10% of the steady state trough after 2 subsequent doses following either a 1-week or 2-week dose delay. Statistically significant covariates included in the final model were body weight, sex, albumin, GEA cancer type, baseline tumor size, and presence of post-baseline anti-drug antibodies, all of which resulted in less than 30% impact on exposure. Model simulation predicts weight-based and two-tiered flat dosing will result in similar exposure and variability. Conclusion The identified significant covariates were not considered clinically meaningful. Both weight-based (30 mg/kg Q3W) and two-tiered flat dosing (1800/2400 mg Q3W, 70 kg threshold) strategies are expected to provide similar exposures of zanidatamab.