An apparent clinical pharmacokinetic drug-drug interaction between bevacizumab and the anti-placental growth factor monoclonal antibody RO5323441 via a target-trapping mechanism.

PURPOSE: RO5323441 is a humanized anti-placental growth factor (PlGF) monoclonal antibody that has shown preclinical activity in several cancer models. The objective of this analysis is to examine the pharmacokinetic (PK) results from four Phase I studies that have been conducted with RO5323441 (n = 61) and to report an apparent drug-drug interaction observed when RO5323441 was administered in combination with bevacizumab. METHODS: The four Phase I studies were a multiple-ascending dose study in 23 patients with solid tumors (Study 1), an open-label study in seven patients with colorectal/ovarian cancer (Study 2), a sorafenib combination study in nine patients with hepatocellular carcinoma (Study 3), and a bevacizumab combination study in 22 patients with recurrent glioblastoma (Study 4). A two-compartment linear population PK model was developed from these four studies to characterize the PK of RO5323441 in patients with cancer. RESULTS: The PK properties of RO5323441 were similar in the first three studies. However, substantially higher RO5323441 exposures were observed in Study 4 when RO5323441 was administered in combination with bevacizumab. A linear two-compartmental population PK model indicated that the co-administration of bevacizumab would decrease the clearance of RO5323441 by 53%. Clinical data suggested that the decrease in RO5323441 clearance was inversely associated with bevacizumab exposure. CONCLUSIONS: The exact reason for the increase in RO5323441 exposure following bevacizumab co-administration is not currently known. One possibility is a drug-drug interaction via a target-trapping mechanism that is mediated by the vascular endothelial growth factor receptor-1 (VEGFR-1).

Methods to detect non-compliance and reduce its impact on population PK parameter estimates.

This work proposes and evaluates two methods (CM1 and CM2) for detecting non-compliance using concentration-time data and for obtaining estimates of population pharmacokinetic model parameters in a population with prevalent non-compliance. CM1 estimates individual residual variability (RV) and identifies subjects with higher than average RV as non-compliant. Exclusion of subjects with high RV from the analysis dataset reduces the bias in the estimates of the model parameters. Various methods of identification and exclusion of non-compliant subjects were tested, compared, and shown to reduce or eliminate bias in parameter estimates associated with non-compliance. The tested methods were (i) a pre-defined cutoff value of the random effect on RV, (ii) sequential exclusion of subjects with the highest RV percentiles, and (iii) use of a mixture model for RV. CM2 is applicable for the data with a specific sampling pattern that includes a potentially non-compliant outpatient part with several trough samples followed by a dense profile after the inpatient (compliant) dose. It relies only on the doses known to be administered (e.g., inpatient doses). In this method, all concentration measurements during the outpatient part of the study (except the trough value immediately preceding the inpatient dose) are removed from the dataset and an additional parameter (individual relative bioavailability of the outpatient doses) is introduced. For a number of simulated datasets with various sampling schemes and non-compliance patterns the proposed methods allowed to identify subjects with compliance problems and to reduce or eliminate bias in the estimates of the model parameters.