Estimation of time to progression and post progression survival using joint modeling of summary level OS and PFS data with an ordinary differential equation model.

measures such as progression-free survival (PFS) and overall survival (OS) are commonly reported in literature for oncology trials, while time to progression (TTP) and post progression survival (PPS) are not usually reported. A time-variant transition hazard model was developed using an ordinary differential equation (ODE) model to estimate TTP and PPS from summary level PFS and OS. The model was applied to published data from immune checkpoint inhibitor trials for non-small cell lung cancer (NSCLC) in a meta-analysis framework. This model-based method was able to robustly estimate TTP and PPS from summary level OS and PFS data, provided a quantitative approach for understanding the patterns of disease progression across different treatments through the time-variant disease progression rate function, and provided a summary of how different treatments affect TTP and PPS. The proposed method can be generalized to characterize and quantify multiple time-to-event endpoints jointly in oncology trials and improve our understanding of disease prognostics for different treatments.

Population pharmacokinetic analysis of esomeprazole in Japanese subjects with various CYP2C19 phenotypes.

WHAT IS KNOWN AND OBJECTIVE: Esomeprazole, the S-isomer of omeprazole, is a proton pump inhibitor which has been approved by over 125 countries, also known as NEXIUM® . Esomeprazole was developed to provide further improvement on efficacy for acid-related diseases with higher systemic bioavailability due to the less first-pass metabolism and lower plasma clearance. Esomeprazole is primarily metabolized by CYP2C19. Approximately <1% of Caucasians and 5%-10% of Asians have absent CYP2C19 enzyme activity. Although the influence of various CYP2C19 phenotypes on esomeprazole pharmacokinetics has been studied, this is the first report in the Japanese population where 27 low CYP2C19 metabolizers were included. METHODS: In this study, a population PK model describing the PK of esomeprazole was developed to understand the difference of CYP2C19 phenotypes on clearance in the Japanese population. The model quantitatively assessed the influence of CYP2C19 phenotype on esomeprazole PK in healthy Japanese male subjects after receiving repeated oral dosing. The inhibition mechanism of esomeprazole on CYP2C19 activity was also included in the model. RESULTS AND DISCUSSION: CYP2C19 phenotype and dose were found as statistically significant covariates on esomeprazole clearance. The apparent clearance at 10-mg dose was 17.32, 9.77 and 7.37 (L/h) for homozygous extensive metabolizer, heterozygous extensive metabolizer and poor metabolizer subjects, respectively. And the apparent clearance decreased as dose increased. WHAT IS NEW AND CONCLUSION: The established population PK model well described the esomeprazole PK and model-predicted esomeprazole PK was in good agreement with external clinical data, suggesting the robustness and applicability of the current model for predicting esomeprazole PK.

Optimal designs for regional bridging studies using the Bayesian power prior method.

As described in the ICH E5 guidelines, a bridging study is an additional study executed in a new geographical region or subpopulation to link or "build a bridge" from global clinical trial outcomes to the new region. The regulatory and scientific goals of a bridging study is to evaluate potential subpopulation differences while minimizing duplication of studies and meeting unmet medical needs expeditiously. Use of historical data (borrowing) from global studies is an attractive approach to meet these conflicting goals. Here, we propose a practical and relevant approach to guide the optimal borrowing rate (percent of subjects in earlier studies) and the number of subjects in the new regional bridging study. We address the limitations in global/regional exchangeability through use of a Bayesian power prior method and then optimize bridging study design with a return on investment viewpoint. The method is demonstrated using clinical data from global and Japanese trials in dapagliflozin for type 2 diabetes.

A multistate platform model for time-to-event endpoints in oncology clinical trials.

A multistate platform model was developed to describe time-to-event (TTE) endpoints in an oncology trial through the following states: initial, tumor response (TR), progressive disease (PD), overall survival (OS) event (death), censor to the last evaluable tumor assessment (progression-free survival [PFS] censor), and censor to study end (OS censor), using an ordinary differential equation framework. Two types of piecewise functions were used to describe the hazards for different events. Piecewise surge functions were used for events that require tumor assessments at the scheduled study visit times (TR, PD, and PFS censor). Piecewise constant functions were used to describe hazards for events that occur evenly throughout the study (OS event and OS censor). The multistate TTE model was applied to describe TTE endpoints from a published phase III study. The piecewise surge functions well-described the observed surges of hazards/events for TR, PD, PFS, and OS occurring near scheduled tumor assessments and showed good agreement with all Kaplan-Meier curves. With the flexibility of piecewise hazard functions, the model was able to evaluate covariate effects in a time-variant fashion to better understand the temporal patterns of disease prognosis through different disease states. This model can be applied to advance the field of oncology trial design and optimization by: (1) enabling robust estimations of baseline hazards and covariate effects for multiple TTE endpoints, (2) providing a platform model for understanding the composition and correlations between different TTE endpoints, and (3) facilitating oncology trial design optimization through clinical trial simulations.

Modeling Tumor Growth and Treatment Resistance Dynamics Characterizes Different Response to Gefitinib or Chemotherapy in Non-Small Cell Lung Cancer.

Differences in the effect of gefitinib and chemotherapy on tumor burden in non-small cell lung cancer remain to be fully understood. Using a Bayesian hierarchical model of tumor size dynamics, we estimated the rates of tumor growth and treatment resistance for patients in the Iressa Pan-Asia Study study (NCT00322452). The following relationships characterize greater efficacy of gefitinib in epidermal growth factor receptor (EGFR) positive tumors: Maximum drug effect is, in decreasing order, gefitinib in EGFR-positive, chemotherapy in EGFR-positive, chemotherapy in EGFR-negative, and gefitinib in EGFR-negative tumors; the rate of resistance emergence is, in increasing order: gefitinib in EGFR positive, chemotherapy in EGFR positive, while each is plausibly similar to the rate in EGFR negative tumors, which are estimated with less certainty. The rate of growth is smaller in EGFR-positive than in EGFR-negative fully resistant tumors, regardless of treatment. The model can be used to compare treatment effects and resistance dynamics among different drugs.

Tumor Growth Dynamic Modeling in Oncology Drug Development and Regulatory Approval: Past, Present, and Future Opportunities.

Model-informed drug development (MIDD) approaches have rapidly advanced in drug development in recent years. Additionally, the Prescription Drug User Fee Act (PDUFA) VI has specific commitments to further enhance MIDD. Tumor growth dynamic (TGD) modeling, as one of the commonly utilized MIDD approaches in oncology, fulfills the purposes to accelerate the drug development, to support new drug and biologics license applications, and to guide the market access. Increasing knowledge of TGD modeling methodologies, encouraging applications in clinical setting for patients' survival, and complementing assessment of regulatory review for submissions, together fueled promising potentials for imminent enhancement of TGD in oncology. This review is to comprehensively summarize the history of TGD, and present case examples of the recent advance of TGD modeling (mixture model and joint model), as well as the TGD impact on regulatory decisions, thus illustrating challenges and opportunities. Additionally, this review presents the future perspectives for TGD approach.