This study describes an important but infrequently used application of population pharmacokinetic/pharmacodynamic modeling to guide dose selection for phase 3 studies of an antiangiogenic agent. Attrition rates for investigational cancer therapeutics are high [27
]. Regulatory guidance and the published literature suggest that the integration of pharmacokinetic, pharmacodynamic, and clinical endpoint data may better inform future study design and help maximize the risk–benefit profile for therapeutics [28
]. In particular, exposure–response modeling may aid in the rational selection of doses for further investigation [31
]. The failure of some cancer therapeutics in development may be due to the conventional approach to dose selection, which primarily focuses on the determination of the maximum tolerated dose [33
], whereas identification of an “optimal biologic dose” may be more appropriate for targeted agents [34
]. Consistent with this approach, a number of recent phase 1 studies have used exposure–response modeling to assess the relationship between exposure and a marker of biologic activity to facilitate dose selection [35
]. However, because these markers have not been clinically validated, the appropriateness of such analyses for use in dose selection has been uncertain [40
The present study was a prospectively planned pharmacokinetic/pharmacodynamic analysis that assessed the relationship between exposure (AUCss) and a key clinical outcome (PFS) to guide dose selection for phase 3 studies of AMG 386 in recurrent ovarian cancer. The population pharmacokinetic part of the analysis revealed that CrCL, a measure of renal function, appears to be a significant covariate for AMG 386 CL. The relationship suggests that renal disposition may play a role in the elimination of AMG 386, which, at a size of approximately 65 kDa, is a fairly large molecule. Renal clearance is uncommon for biologic therapeutics, such as monoclonal antibodies, and, to our knowledge, has not been described previously. Estimated glomerular filtration rate (calculated using the Modification of Diet in Renal Disease, MDRD, method), which is another measure of renal function, also showed a significant effect on AMG 386 CL (data not shown). This further supports our finding that the kidney may be implicated in the elimination of AMG 386. However, the exact mechanism of the effect of CrCL on the CL of AMG 386 remains uncertain and warrants further investigation.
Exposure–response analysis revealed a robust relationship between AMG 386 exposure and PFS, suggesting that maximum clinical benefit was not reached at a dose of 10 mg/kg QW. The exposure-PFS relationship remained after adjusting for potential confounding factors in the multivariate analysis. However, given the relatively small sample size of the phase 2 study, other unknown confounding factors may have introduced an unidentified bias. Using the results from the simulations based on the parametric survival model, an AMG 386 dose of 15 mg/kg QW in combination with cytotoxic chemotherapy has been proposed for phase 3 studies in patients with recurrent ovarian cancer (TRINOVA-1 [ClinicalTrials.gov, NCT01204749] and TRINOVA-2 [ClinicalTrials.gov, NCT01281254]). Although the toxicity of this dose when combined with paclitaxel has not yet been directly tested, the exposure-safety analysis presented here suggests that 15 mg/kg of AMG 386 will have a similar safety profile as the 10 mg/kg dose. There were no marked differences in the incidence of grade ≥ 3 AEs between patients with high and low AMG 386 exposure, and the primary analysis did not show any apparent dose-related trends in toxicity when comparing 3 and 10 mg/kg QW administered in combination with paclitaxel [17
]. In the phase 1 study, 30 mg/kg QW (the maximum tested dose) was well tolerated as monotherapy [18
Exposure–response relationships appear to be influenced by a number of factors, which can complicate efforts to identify an optimal biological exposure (OBE) and optimal biological dose (OBD) for a given anticancer agent. OBDs and OBEs from monotherapy dose-escalation studies in mixed solid tumors may not translate into later-stage studies (monotherapy or combination therapy) of single tumor types. For example, not all clinical studies of the anti-VEGF-A antibody bevacizumab have shown a consistent dose–response relationship, suggesting that different optimum doses may be needed for different tumor types or disease characteristics [41
]. Furthermore, an agent’s OBE may differ between in vitro models and clinical studies. The AMG 386 OBE for antitumor efficacy in xenograft models [16
] appeared to be lower than that identified in the phase 2 ovarian cancer study. This may reflect differences in receptor occupancy across species, which has been observed in other contexts [42
Our study demonstrates the use of a novel model-based approach to dose selection for a phase 3 study of an investigational targeted therapy. Applying this technique to the decision-making process in the development of anticancer agents, for which dose-ranging studies are rarely performed [8
], provides important opportunities. Integrating results from preclinical pharmacokinetic, pharmacologic, and toxicity studies into appropriate models can guide the design of early clinical studies and inform the interpretation of its results, thus supporting the fast transition of a promising molecule from discovery into the clinic. Go/no-go decisions during continued clinical development and dose selection for late-stage studies can also be successfully supported by modeling applications. Thus, quantitative (such as pharmacokinetic/pharmacodynamic and/or exposure–response) modeling and simulations can guide each step of a clinical development plan from early discovery through pivotal phase 3 studies [1
]. However, this approach is often limited because it requires early integration of pharmacometric scientists in the clinical decision-making process as well as the timely development of relevant models.
In summary, our study demonstrates how exposure–response analyses of phase 2 study data and the application of pharmacokinetic/pharmacodynamic models can assist in the selection of doses for subsequent phase 3 studies of an antiangiogenic therapeutic.