Jian-Feng Lu

Simulations Using a Drug–Disease Modeling Framework and Phase II Data Predict Phase III Survival Outcome in First‐Line Non–Small‐Cell Lung Cancer

Simulations were performed for carboplatin/paclitaxel (C/P) plus motesanib or bevacizumab vs. C/P as first‐line treatment for advanced non–small‐cell lung cancer (NSCLC) using a published drug–disease model. With 700 patients in each arm, simulated hazard ratios for motesanib (0.87; 95% confidence interval [CI], 0.71–1.1) and bevacizumab (0.89; 95% CI, 0.73–1.1) agreed with results from the respective phase III studies but did not discriminate between failed and successful studies. The current model may require further enhancement to improve its utility for predicting phase III outcomes.

A Modeling and Simulation Framework to Support Early Clinical Drug Development Decisions in Oncology

• J Clin Pharmacol 2011;51:6-8 T success rate of new molecules in oncology is the lowest in any therapeutic area. The high failure rate and cost associated with oncology drug development may reflect poor predictability of in vivo preclinical tumor xenograft models and lack of quantitative approaches to guide both preclinical and clinical development. Antitumor activity in early clinical studies is typically evaluated using objective response rate (ORR) or progressionfree survival (PFS). However, these estimates in typical small, noncomparative phase 1 or phase 2 trials are generally imprecise and uninformative to efficiently support go–no go decisions and design of phase 3 clinical trials. The phase 3 trial failure rate is particularly high in oncology, and there clearly is a need for more quantitative approaches to improve the success rate of oncology drugs consistent with the FDA recent initiatives. To address these issues, we are developing a drugdisease modeling framework that has been successfully applied to predict expected clinical response and survival in cancer patients in a number of clinical settings. This modeling framework focuses on efficacy, and the core of this framework is constituted by an exposure-driven tumor growth inhibition (TGI) model that uses the full longitudinal tumor size data as opposed to categorizing data, as in the calculation of ORR. Change in tumor size from baseline is used as a biomarker of drug effect to predict survival in a drug-independent survival model. It is therefore an informative and predictive patient-level continuous end point that can be assessed in early phase 1 or 2 clinical studies. The proposed modeling framework (drug-specific TGI model coupled with drugindependent survival model) can enhance learning from early clinical studies compared with the traditional approach of estimating ORR and PFS.

Exploratory Modeling and Simulation to Support Development of Motesanib in Asian Patients With Non–Small Cell Lung Cancer Based on MONET1 Study Results

The motesanib phase III MONET1 study failed to show improvement in overall survival (OS) in non–small cell lung cancer, but a subpopulation of Asian patients had a favorable outcome. We performed exploratory modeling and simulations based on MONET1 data to support further development of motesanib in Asian patients. A model‐based estimate of time to tumor growth was the best of tested tumor size response metrics in a multivariate OS model (P < 0.00001) to capture treatment effect (hazard ratio, HR) in Asian patients. Significant independent prognostic factors for OS were baseline tumor size (P < 0.0001), smoking history (P < 0.0001), and ethnicity (P < 0.00001). The model successfully predicted OS distributions and HR in the full population and in Asian patients. Simulations indicated that a phase III study in 500 Asian patients would exceed 80% power to confirm superior efficacy of motesanib combination therapy (expected HR: 0.74), suggesting that motesanib combination therapy may benefit Asian patients.

FLT3 and CDK4/6 inhibitors: Signaling mechanisms and tumor burden in subcutaneous and orthotopic mouse models of acute myeloid leukemia

FLT3ITD subtype acute myeloid leukemia (AML) has a poor prognosis with currently available therapies. A number of small molecule inhibitors of FLT3 and/or CDK4/6 are currently under development. A more complete and quantitative understanding of the mechanisms of action of FLT3 and CDK4/6 inhibitors may better inform the development of current and future compounds that act on one or both of the molecular targets, and thus may lead to improved treatments for AML. In this study, we investigated in both subcutaneous and orthotopic AML mouse models, the mechanisms of action of three FLT3 and/or CDK4/6 inhibitors: AMG925 (Amgen), sorafenib (Bayer and Onyx), and quizartinib (Ambit Biosciences). A composite model was developed to integrate the plasma pharmacokinetics of these three compounds on their respective molecular targets, the coupling between the target pathways, as well as the resulting effects on tumor burden reduction in the subcutaneous xenograft model. A sequential modeling approach was used, wherein model structures and estimated parameters from upstream processes (e.g. PK, cellular signaling) were fixed for modeling subsequent downstream processes (cellular signaling, tumor burden). Pooled data analysis was employed for the plasma PK and cellular signaling modeling, while population modeling was applied to the tumor burden modeling. The resulting model allows the decomposition of the relative contributions of FLT3ITD and CDK4/6 inhibition on downstream signaling and tumor burden. In addition, the action of AMG925 on cellular signaling and tumor burden was further studied in an orthotopic tumor mouse model more closely representing the physiologically relevant environment for AML.

Differential Pharmacokinetics of Ganitumab in Patients With Metastatic Pancreatic Cancer Versus Other Advanced Solid Cancers

Ganitumab is an investigational, fully human monoclonal antibody antagonist of the insulin‐like growth factor‐1 receptor (IGF1R) that has shown trends towards improved progression‐free survival and overall survival in a phase 2 pancreatic cancer clinical trial. To characterize ganitumab pharmacokinetics (PK) and identify factors affecting PK, ganitumab serum concentration data from three clinical trials were analyzed. The PK of ganitumab as monotherapy and in combination with gemcitabine in patients with pancreatic or non‐pancreatic cancer were assessed with a non‐linear mixed‐effect model. We found that ganitumab exhibited linear and time‐invariant kinetics. A two‐compartment model adequately described data over a dose range of 1–20 mg/kg with good predictive capability. Typical clearance and central volume of distribution values were 1.7‐ and 1.3‐fold higher, respectively, in patients with pancreatic cancer than in patients with other advanced solid cancers, resulting in lower ganitumab exposure. Covariate analysis was used to evaluate effects of cancer type, gemcitabine coadministration, clinical study, demographics, and laboratory values on ganitumab PK. Pancreatic cancer type was the most significant covariate on clearance along with weight, albumin, and serum creatinine. Gemcitabine coadministration did not affect ganitumab clearance. Thus, disease state can significantly affect PK and should be considered when selecting the clinically effective dose.

Population pharmacokinetic modeling of motesanib and its active metabolite, M4, in cancer patients

Motesanib is a small molecule and potent multikinase inhibitor with antiangiogenic and antitumor activity. Population pharmacokinetic (POPPK) modeling of motesanib and M4, an active metabolite, was performed to assess sources of variability in cancer patients. The analysis included data collected from 451 patients from 8 clinical trials with oral doses of motesanib ranging from 25 to 175 mg, either once daily or twice daily. The POPPK analyses were performed using nonlinear mixed‐effect models with a sequential approach. Covariate effects of demographics and other baseline characteristics were assessed with stepwise covariate modeling. A 2‐compartment model with food effect on absorption parameters fitted the PK data of motesanib well. The effects albumin and sex on apparent clearance (CL/F) of motesanib were statistically significant. The albumin effect was more important but remained below a 25% difference. A 1‐compartment model fitted PK data of M4 well. Effects of race (Asian vs non‐Asian) and dosing frequency were identified as statistically significant covariates on the CL/F of M4. The maximum effect of albumin would result in less than 25% change in motesanib CL/F and as such would not warrant any dosing adjustment. However, faster elimination of M4 in Asian patients requires further investigation.