On the Use of Change in Tumor Size to Predict Survival in Clinical Oncology Studies: Toward a New Paradigm to Design and Evaluate Phase II Studies

Early Decision Making in a Randomized Phase II Trial of Atezolizumab in Biliary Tract Cancer Using a Tumor Growth Inhibition-Survival Modeling Framework

We assess the longitudinal tumor growth inhibition (TGI) metrics and overall survival (OS) predictions applied to patients with advanced biliary tract cancer (BTC) enrolled in IMbrave151 a multicenter randomized phase II, double-blind, placebo-controlled trial evaluating the efficacy and safety of atezolizumab with or without bevacizumab in combination with cisplatin plus gemcitabine. Tumor growth rate (KG) was estimated for patients in IMbrave151. A pre-existing TGI-OS model for patients with hepatocellular carcinoma in IMbrave150 was modified to include available IMbrave151 study covariates and KG estimates and used to simulate IMbrave151 study outcomes. At the interim progression-free survival (PFS) analysis (98 patients, 27 weeks follow-up), clear separation in tumor dynamic profiles with a faster shrinkage rate and slower KG (0.0103 vs. 0.0117 week-1 ; tumor doubling time 67 vs. 59 weeks; KG geometric mean ratio of 0.84) favoring the bevacizumab containing arm was observed. At the first interim analysis for PFS, the simulated OS hazard ratio (HR) 95% prediction interval (PI) of 0.74 (95% PI: 0.58-0.94) offered an early prediction of treatment benefit later confirmed at the final analysis, observed HR of 0.76 based on 159 treated patients and 34 weeks of follow-up. This is the first prospective application of a TGI-OS modeling framework supporting gating of a phase III trial. The findings demonstrate the utility for longitudinal TGI and KG geometric mean ratio as relevant end points in oncology studies to support go/no-go decision making and facilitate interpretation of the IMbrave151 results to support future development efforts for novel therapeutics for patients with advanced BTC.

Application of time-dependent modeling for the exposure-efficacy analysis of ceritinib in untreated ALK-rearranged advanced NSCLC patients

PURPOSE

Ceritinib 750 mg/day was approved for the treatment of patients with untreated anaplastic lymphoma kinase (ALK)-rearranged non-small cell lung cancer (NSCLC) based on ASCEND-4 study. The objective of this article is to introduce the use of time-dependent modeling approach in the updated exposure-efficacy analysis of ceritinib for the first-line indication.

METHODS

Exposure-efficacy analyses, including data from 156 patients, were first conducted using time-independent logistic regression model for response of complete or partial response and Cox regression model for progression-free survival (PFS). The exposure measure used was average C_trough, which is defined as the geometric mean of all evaluable C_trough for each patient. To further investigate the impact of exposure measure on exposure-efficacy analyses, a time-dependent modeling approach was used, where exposure at different time intervals was associated with the corresponding response endpoints in a longitudinal manner.

RESULTS

With exposure measure being average C_trough, it was observed that higher exposure was associated with reduced efficacy in terms of response (odds ratio = 0.77) and PFS [hazard ratio (HR) = 1.12]. These time-independent models do not account for the impact of time-varying concentration due to dose modifications. Subsequently, a new time-dependent modeling approach was used, where exposure and efficacy were associated longitudinally in the analyses. The results showed that the odds ratio of response became 1.07, and the HR of PFS became 1.04, indicating no apparent reverse relationship between exposure and efficacy across the exposure range studied.

CONCLUSION

The drug effect on efficacy in clinical trials could be better characterized using time-dependent exposure-response models.

Pembrolizumab Exposure-Response Assessments Challenged by Association of Cancer Cachexia and Catabolic Clearance

PURPOSE

To investigate the relationship of pembrolizumab pharmacokinetics (PK) and overall survival (OS) in patients with advanced melanoma and non-small cell lung cancer (NSCLC).

PATIENTS AND METHODS

PK dependencies in OS were evaluated across three pembrolizumab studies of either 200 mg or 2 to 10 mg/kg every 3 weeks (Q3W). Kaplan-Meier plots of OS, stratified by dose, exposure, and baseline clearance (CL₀), were assessed per indication and study. A Cox proportional hazards model was implemented to explore imbalances of typical prognostic factors in high/low NSCLC CL₀ subgroups.

RESULTS

A total of 1,453 subjects were included: 340 with pembrolizumab-treated melanoma, 804 with pembrolizumab-treated NSCLC, and 309 with docetaxel-treated NSCLC. OS was dose independent from 2 to 10 mg/kg for pembrolizumab-treated melanoma [HR = 0.98; 95% confidence interval (CI), 0.94-1.02] and NSCLC (HR = 0.98; 95% CI, 0.95-1.01); however, a strong CL₀-OS association was identified for both cancer types (unadjusted melanoma HR = 2.56; 95% CI, 1.72-3.80 and NSCLC HR = 2.64; 95% CI, 1.94-3.57). Decreased OS in subjects with higher pembrolizumab CL₀ paralleled disease severity markers associated with end-stage cancer anorexia-cachexia syndrome. Correction for baseline prognostic factors did not fully attenuate the CL₀-OS association (multivariate-adjusted CL₀ HR = 1.64; 95% CI, 1.06-2.52 for melanoma and HR = 1.88; 95% CI, 1.22-2.89 for NSCLC).

CONCLUSIONS

These data support the lack of dose or exposure dependency in pembrolizumab OS for melanoma and NSCLC between 2 and 10 mg/kg. An association of pembrolizumab CL₀ with OS potentially reflects catabolic activity as a marker of disease severity versus a direct PK-related impact of pembrolizumab on efficacy. Similar data from other trials suggest such patterns of exposure-response confounding may be a broader phenomenon generalizable to antineoplastic mAbs.See related commentary by Coss et al., p. 5787.

A predictive probability interim design for phase II clinical trials with continuous endpoints

Molecular targeted therapies come often with lower toxicity profiles than traditional cytotoxic treatments, thus shifting drug development paradigm into establishing evidence of biological activity, target modulation, and pharmacodynamics effects of these therapies in early phase trials. Therefore, these trials need to address simultaneous evaluation of safety, proof-of-concept biological marker activity, or changes in continuous tumor size instead of binary response rate. Interim analyses are typically incorporated in the trial due to concerns regarding excessive toxicity and ineffective new treatment. There is a lack of interim strategies developed to monitor futility and/or efficacy for these types of continuous outcomes, especially in single-arm phase II trials. We propose a 2-stage design based on predictive probability to accommodate continuous endpoints, assuming a normal distribution with known variance. Simulation results and case study demonstrated that the proposed design can incorporate an interim stop for futility as well as for efficacy while maintaining desirable design properties. As expected, using continuous tumor size resulted in reduced sample sizes for both optimal and minimax designs. A limited exploration of various priors was performed and shown to be robust. As research rapidly moves to incorporate more molecular targeted therapies, it will accommodate new types of outcomes while allowing for flexible stopping rules to continue optimizing trial resources and prioritize agents with compelling early phase data.

Assessing Similarity Among Individual Tumor Size Lesion Dynamics: The CICIL Methodology

Mathematical models of tumor dynamics generally omit information on individual target lesions (iTLs), and consider the most important variable to be the sum of tumor sizes (TS). However, differences in lesion dynamics might be predictive of tumor progression. To exploit this information, we have developed a novel and flexible approach for the non‐parametric analysis of iTLs, which integrates knowledge from signal processing and machine learning. We called this new methodology ClassIfication Clustering of Individual Lesions (CICIL). We used CICIL to assess similarities among the TS dynamics of 3,223 iTLs measured in 1,056 patients with metastatic colorectal cancer treated with cetuximab combined with irinotecan, in two phase II studies. We mainly observed similar dynamics among lesions within the same tumor site classification. In contrast, lesions in anatomic locations with different features showed different dynamics in about 35% of patients. The CICIL methodology has also been implemented in a user‐friendly and efficient Java‐based framework.

Model-Based Meta-Analysis for Multiple Myeloma: A Quantitative Drug-Independent Framework for Efficient Decisions in Oncology Drug Development

The failure rate for phase III trials in oncology is high; quantitative predictive approaches are needed. We developed a model‐based meta‐analysis (MBMA) framework to predict progression‐free survival (PFS) from overall response rates (ORR) in relapsed/refractory multiple myeloma (RRMM), using data from seven phase III trials. A Bayesian analysis was used to predict the probability of technical success (PTS) for achieving desired phase III PFS targets based on phase II ORR data. The model demonstrated a strongly correlated (R² = 0.84) linear relationship between ORR and median PFS. As a representative application of the framework, MBMA predicted that an ORR of ∼66% would be needed in a phase II study of 50 patients to achieve a target median PFS of 13.5 months in a phase III study. This model can be used to help estimate PTS to achieve gold‐standard targets in a target product profile, thereby enabling objectively informed decision‐making.

Exposure-Response Analysis of Necitumumab Efficacy in Squamous Non-Small Cell Lung Cancer Patients

We sought to describe the exposure-response relationship of necitumumab efficacy in squamous non-small cell lung cancer patients and evaluate intrinsic and extrinsic patient descriptors that may guide dosing. SQUIRE was a phase III study comparing necitumumab in combination with gemcitabine and cisplatin vs. gemcitabine and cisplatin alone in 1,014 patients. An integrated model for tumor size dynamics and overall survival was developed, where reduction in tumor size results in a decrease in survival hazard. The change in tumor size was characterized using linear growth and first-order shrinkage. Overall survival was described using a combination of a Weibull function and Gompertz function for the hazard, with dynamic tumor size being a predictor for the hazard. Although body weight resulted in higher clearance and lower exposure, simulations showed that an 800 mg flat dose provided optimal response regardless of body weight.

A Review of Modeling Approaches to Predict Drug Response in Clinical Oncology

Model-based approaches have emerged as important tools for quantitatively understanding temporal relationships between drug dose, concentration, and effect over the course of treatment, and have now become central to optimal drug development and tailored drug treatment. In oncology, the therapeutic index of a chemotherapeutic drug is typically narrow and a full dose-response relationship is not available, often because of treatment failure. Noting the benefits of model-based approaches and the low therapeutic index of oncology drugs, in recent years, modeling approaches have been increasingly used to streamline oncologic drug development through early identification and quantification of dose-response relationships. With this background, this report reviews publications that used model-based approaches to evaluate drug treatment outcome variables in oncology therapeutics, ranging from tumor size dynamics to tumor/biomarker time courses and survival response.

Simulations to Predict Clinical Trial Outcome of Bevacizumab Plus Chemotherapy vs. Chemotherapy Alone in Patients With First-Line Gastric Cancer and Elevated Plasma VEGF-A

To simulate clinical trials to assess overall survival (OS) benefit of bevacizumab in combination with chemotherapy in selected patients with gastric cancer (GC), a modeling framework linking OS with tumor growth inhibition (TGI) metrics and baseline patient characteristics was developed. Various TGI metrics were estimated using TGI models and data from two phase III studies comparing bevacizumab plus chemotherapy vs. chemotherapy as first-line therapy in 976 GC patients. Time-to-tumor-growth (TTG) was the best TGI metric to predict OS. TTG, Eastern Cooperative Oncology Group (ECOG) score, albumin level, and Asian ethnicity were significant covariates in the final OS model. The model correctly predicted a decreased hazard ratio favorable to bevacizumab in patients with high baseline plasma VEGF-A above the median of 113.4 ng/L. Based on trial simulations, in trials enrolling patients with elevated baseline plasma VEGF-A (500 patients per arm), the expected hazard ratio was 0.82 (95% prediction interval: 0.70-0.95), independent of ethnicity.

The Food and Drug Administration Office of Women's Health: Impact of Science on Regulatory Policy: An Update

The U.S. Food and Drug Administration Office of Women's Health (FDA OWH) has supported women's health research for ∼20 years, funding more than 300 studies on women's health issues, including research on diseases/conditions that disproportionately affect women in addition to the evaluation of sex differences in the performance of and response to medical products. These important women's health issues are studied from a regulatory perspective, with a focus on improving and optimizing medical product development and the evaluation of product safety and efficacy in women. These findings have influenced industry direction, labeling, product discontinuation, safety notices, and clinical practice. In addition, OWH-funded research has addressed gaps in the knowledge about diseases and medical conditions that impact women across the life span such as cardiovascular disease, pregnancy, menopause, osteoporosis, and the safe use of numerous medical products.

Comparative Effects of CT Imaging Measurement on RECIST End Points and Tumor Growth Kinetics Modeling

Quantitative assessments of tumor burden and modeling of longitudinal growth could improve phase II oncology trials. To identify obstacles to wider use of quantitative measures we obtained recorded linear tumor measurements from three published lung cancer trials. Model-based parameters of tumor burden change were estimated and compared with similarly sized samples from separate trials. Time-to-tumor growth (TTG) was computed from measurements recorded on case report forms and a second radiologist blinded to the form data. Response Evaluation Criteria in Solid Tumors (RECIST)-based progression-free survival (PFS) measures were perfectly concordant between the original forms data and the blinded radiologist re-evaluation (intraclass correlation coefficient = 1), but these routine interrater differences in the identification and measurement of target lesions were associated with an average 18-week delay (range, -20 to 55 weeks) in TTG (intraclass correlation coefficient = 0.32). To exploit computational metrics for improving statistical power in small clinical trials will require increased precision of tumor burden assessments.

Population pharmacokinetic-pharmacodynamic modelling in oncology: a tool for predicting clinical response

In oncology trials, overall survival (OS) is considered the most reliable and preferred endpoint to evaluate the benefit of drug treatment. Other relevant variables are also collected from patients for a given drug and its indication, and it is important to characterize the dynamic effects and links between these variables in order to improve the speed and efficiency of clinical oncology drug development. However, the drug-induced effects and causal relationships are often difficult to interpret because of temporal differences. To address this, population pharmacokinetic–pharmacodynamic (PKPD) modelling and parametric time-to-event (TTE) models are becoming more frequently applied. Population PKPD and TTE models allow for exploration towards describing the data, understanding the disease and drug action over time, investigating relevance of biomarkers, quantifying patient variability and in designing successful trials. In addition, development of models characterizing both desired and adverse effects in a modelling framework support exploration of risk-benefit of different dosing schedules. In this review, we have summarized population PKPD modelling analyses describing tumour, tumour marker and biomarker responses, as well as adverse effects, from anticancer drug treatment data. Various model-based metrics used to drive PD response and predict OS for oncology drugs and their indications are also discussed.

A Tumor Growth Inhibition Model Based on M-Protein Levels in Subjects With Relapsed/Refractory Multiple Myeloma Following Single-Agent Carfilzomib Use

Change in tumor size estimated using longitudinal tumor growth inhibition (TGI) modeling is an early predictive biomarker of clinical outcomes for multiple cancer types. We present the application of TGI modeling for subjects with multiple myeloma (MM). Longitudinal time course changes in M‐protein data from relapsed and/or refractory MM subjects who received single‐agent carfilzomib in phase II studies (n = 456) were fit to a TGI model. The tumor growth rate estimate was similar to that of other anti‐myeloma agents, indicating that the model is robust and treatment‐independent. An overall survival model was subsequently developed, which showed that early change in tumor size (ECTS) at week 4, Eastern Cooperative Oncology Group performance status (ECOG PS), hemoglobin, sex, percent bone marrow cell involvement, and number of prior regimens were significant independent predictors for overall survival (P < 0.001). ECTS based on M‐protein modeling could be an early biomarker for survival in MM following exposure to single‐agent carfilzomib.

Population pharmacokinetics and pharmacodynamics of BYL719, a phosphoinositide 3-kinase antagonist, in adult patients with advanced solid malignancies

AIMS

The aim was to characterize the population pharmacokinetics of BYL719 in cancer patients and assess the time course of tumour response in relation to drug exposure and dosing schedule.

METHODS

Plasma samples and longitudinal tumour size measurements were collected from 60 patients with advanced solid malignancies who received oral BYL719 once daily (30-450 mg) or twice daily at 120 mg or 200 mg. Non-linear mixed effect modelling was employed to develop the population pharmacokinetic and pharmacodynamic model.

RESULTS

The pharmacokinetics were best described by a one compartment disposition model and transit compartments accounting for the lag time in absorption. The typical population oral clearance and volume of distribution estimates with their between-subject variability (BSV) were 10 l h(-1) (BSV 26%) and 108 l (BSV 28%), respectively. The estimated optimal number of transit compartments was 8.1, with a mean transit time to the absorption compartment of 1.28 h (BSV 32%). The between-occasion variability in the rate and extent of absorption was 46% and 26%, respectively. Tumour growth was modelled using a turnover model characterized by a zero order growth rate of 0.581 cm week(1) and a first order death rate of 0.0123 week(-1) . BYL719 inhibited tumour growth with an IC50 of 100 ng ml(-1) (BSV 154%). Model-based predictions showed potential for additional anti-tumour activity of twice daily dosing at total daily dose below 400 mg, but a loss of efficacy if administered less frequently than once daily.

CONCLUSIONS

The proposed model provides a valuable approach for planning future clinical studies and for designing optimized dosing regimens with BYL719.

The effectiveness of RECIST on survival in patients with NSCLC receiving chemotherapy with or without target agents as first-line treatment

We analyzed the correlation between survival and antitumor effect evaluated by RECIST in advanced NSCLC patients with chemotherapy plus target therapy or not as first-line treatment, to examine the applicability of RECIST in this population. The patients were screened from 4 clinical trials (12621, 12006, FASTACT-I, and FASTACT-II), and those who received chemotherapy plus target therapy or chemotherapy alone were eligible. Among the 59 enrolled patients, 29 received combination therapy, while the other 30 received chemotherapy only. In the combination therapy group, patients with PR or SD had longer overall survival (OS) than those with PD (P < 0.001 and P = 0.002, respectively). However, in the chemotherapy alone group, compared with PD patients, either PR or SD group had no significant overall survival benefit (P = 0.690 and P = 0.528, respectively). In summary, for advanced NSCLC patients receiving chemotherapy plus target therapy as first-line treatment and evaluated by RECIST criteria, SD has the same overall survival benefit as PR, suggesting that antitumor effective evaluation by RECIST criteria cannot be translated to overall survival benefit especially for this kind of patients. Therefore, developing a more comprehensive evaluation method to perfect RECIST criteria is thus warranted for patients received target therapy in NSCLC.

Optimizing oncology therapeutics through quantitative translational and clinical pharmacology: challenges and opportunities

Despite advances in biomedical research that have deepened our understanding of cancer hallmarks, resulting in the discovery and development of targeted therapies, the success rates of oncology drug development remain low. Opportunities remain for objective dose selection informed by exposure-response understanding to optimize the benefit-risk balance of novel therapies for cancer patients. This review article discusses the principles and applications of modeling and simulation approaches across the lifecycle of development of oncology therapeutics. Illustrative examples are used to convey the value gained from integration of quantitative clinical pharmacology strategies from the preclinical-translational phase through confirmatory clinical evaluation of efficacy and safety.

A review of mixed-effects models of tumor growth and effects of anticancer drug treatment used in population analysis

Population modeling of tumor size dynamics has recently emerged as an important tool in pharmacometric research. A series of new mixed-effects models have been reported recently, and we present herein a synthetic view of models with published mathematical equations aimed at describing the dynamics of tumor size in cancer patients following anticancer drug treatment. This selection of models will constitute the basis for the Drug Disease Model Resources (DDMoRe) repository for models on oncology.

Optimizing drug development of anti-cancer drugs in children using modelling and simulation

Modelling and simulation (M&S)-based approaches have been proposed to support paediatric drug development in order to design and analyze clinical studies efficiently. Development of anti-cancer drugs in the paediatric population is particularly challenging due to ethical and practical constraints. We aimed to review the application of M&S in the development of anti-cancer drugs in the paediatric population, and to identify where M&S-based approaches could provide additional support in paediatric drug development of anti-cancer drugs. A structured literature search on PubMed was performed. The majority of identified M&S-based studies aimed to use population PK modelling approaches to identify determinants of inter-individual variability, in order to optimize dosing regimens and to develop therapeutic drug monitoring strategies. Prospective applications of M&S approaches for PK-bridging studies have scarcely been reported for paediatric oncology. Based on recent developments of M&S in drug development there are several opportunities where M&S could support more informative bridging between children and adults, and increase efficiency of the design and analysis of paediatric clinical trials, which should ultimately lead to further optimization of drug treatment strategies in this population.

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.

Prediction of overall survival or progression free survival by disease control rate at week 8 is independent of ethnicity: Western versus Chinese patients with first-line non-small cell lung cancer treated with chemotherapy with or without bevacizumab

Categorizations of best response observed at week 8 (between week 3 and 14) of first-line treatment in two studies of bevacizumab plus chemotherapy in Western (878 patients) and Chinese (198 patients) patients with non-small cell lung cancer were assessed together with baseline prognostic factors in multivariate parametric models to predict overall survival (OS) and progression free survival (PFS). Predictive performances of the models were assessed by simulating multiple replicates of the studies. Disease control rate (DCR) was the best response categorization to predict OS and PFS. In the OS model, DCR fully captured bevacizumab effect. For PFS, DCR did not fully capture bevacizumab treatment effect. The models adequately predicted OS and PFS distributions in each arm as well as bevacizumab hazard ratio (HR) for OS and PFS, for example, in Western patients (model prediction [95% prediction interval]: 0.84 [0.71-0.98] vs. observed: 0.77 for OS and 0.59 [0.49-0.72] vs. observed: 0.58 for PFS). Covariates in the models captured endpoint differences seen in Chinese patients. There was no impact of Chinese ethnicity on the DCR relationship to OS or PFS. DCR predicted OS benefit with bevacizumab in first-line NSCLC patients. Western data can be used to inform design of studies in Chinese patients.

Considerations for the prediction of survival time in pancreatic cancer based on registry data

Semi-parametric and parametric survival models in patients with pancreatic adenocarcinoma (PC) using data from Surveillance, Epidemiology, and End Result (SEER) registry were developed to identify relevant covariates affecting survival, verify against external patient data and predict disease outcome. Data from 82,251 patients was extracted using site and histology codes for PC in the SEER database and refined based on specific cause of death. Predictors affecting survival were selected from SEER database; the analysis dataset included 2,437 patients. Survival models were developed using both semi-parametric and parametric approaches, evaluated using Cox-Snell and deviance residuals, and predictions were assessed using an external dataset from Saint Louis University (SLU). Prediction error curves (PECs) were used to evaluate prediction performance of these models compared to Kaplan-Meier response. Median overall survival time of patients from SEER data was 5 months. Our analysis shows that the PC data from SEER was best fitted by both semi-parametric and the parametric model with log-logistic distribution. Predictors that influence survival included disease stage, grade, histology, tumor size, radiation, chemotherapy, surgery, and lymph node status. Survival time predictions from the SLU dataset were comparable and PECs show that both semi-parametric and parametric models exhibit similar predictive performance. PC survival models constructed from registry data can provide a means to classify patients into risk-based subgroups, to predict disease outcome and aide in the design of future prospective randomized trials. These models can evolve to incorporate predictive biomarker and pharmacogenetic correlates once adequate causal data is established.

Evaluation of tumor-size response metrics to predict overall survival in Western and Chinese patients with first-line metastatic colorectal cancer

PURPOSE

To assess new metrics of tumor-size response to predict overall survival (OS) in colorectal cancer (CRC) in Western and Chinese patients.

PATIENTS AND METHODS

Various metrics of tumor-size response were estimated using longitudinal tumor size models and data from two phase III studies that compared bevacizumab plus chemotherapy versus chemotherapy as first-line therapy in Western (n = 923) and Chinese (n = 203) patients with CRC. Baseline prognostic factors and tumor-size metrics estimates were assessed in multivariate models to predict OS. Predictive performances of the models were assessed by simulating multiple replicas of the phase III studies.

RESULTS

Time to tumor growth (TTG) was the best metric to predict OS. TTG fully captured bevacizumab effect. Chinese ethnicity had no impact on OS or on the TTG-OS relationships. The model correctly predicted OS distributions in each arm as well as bevacizumab hazard ratio (model prediction, 0.75 v 0.68 observed in Western patients; 95% prediction interval, 0.62 to 0.91).

CONCLUSION

TTG captured therapeutic benefit with bevacizumab in first-line CRC patients. Chinese ethnicity had no impact. Longitudinal tumor size data coupled with model-based approaches may offer a powerful alternative in the design and analysis of early clinical studies.

Modeling NSCLC progression: recent advances and opportunities available

Non-small cell lung cancer (NSCLC) is one of the leading causes of death around the world with an estimated 5-year relative survival rate of 16% at diagnosis. Development of drugs treating NSCLC is not easy, and the success rate for an anticancer treatment to pass through the whole clinical development process is as low as 5%. Modeling and simulation lend themselves as tools which can potentially streamline drug development. A critical component of the models developed is a description of how the disease progresses over time and how a treatment would affect its trajectory. Our aim was to review the literature to present the models and growth functions which have been used for describing NSCLC dynamics, and how anticancer treatments can affect such dynamics, both in animals and in humans. Only a limited set of models were identified for such a purpose. Most of the models which have been used were descriptive of tumor growth, yet there were attempts to account for the underlying processes, especially in animals where it is more feasible to collect data needed for developing such models. Moreover, we discuss how modeling and simulation can aid in decision making across the different stages of drug development. Based on some encouraging results from trials of other cancer types where modeling tumor dynamics has played an important role, we propose further exploration of NSCLC using model-based techniques and further use of these techniques in designing and evaluating NSCLC trials.

Simulations to Assess Phase II Noninferiority Trials of Different Doses of Capecitabine in Combination With Docetaxel for Metastatic Breast Cancer

A phase II trial in metastatic breast cancer (MBC) (NO16853) failed to show noninferiority (progression-free survival, PFS) of capecitabine 825 mg/m² plus docetaxel 75 mg/m² to the registered capecitabine dose of 1,250 mg/m² plus docetaxel 75 mg/m². We developed a modeling framework based on NO16853 and the pivotal phase III MBC study, SO14999, to characterize the link between capecitabine dose, tumor growth, PFS, and survival to simulate response to a range of capecitabine doses and determine a minimum capecitabine dose noninferior to 1,250 mg/m². Simulation showed NO16853 had little power to demonstrate noninferiority (69%). The power reached 80% with a 1,000 mg/m² starting dose and an increased number of PFS events. A starting dose of 1,000 mg/m² could be established as noninferior in terms of efficacy to the registered dose in the second-line MBC setting, with a potentially improved safety, in line with medical practice.

Clinical trials in the era of personalized oncology

The rapid pace of discoveries in tumor biology, imaging technology, and human genetics hold promise for an era of personalized oncology care. The successful development of a handful of new targeted agents has generated much hope and hype about the delivery of safer and more effective new treatments for cancer. The design and conduct of clinical trials has not yet adjusted to a new era of personalized oncology and so we are more in transition to that era than in it. With the development of treatments for breast cancer as a model, we review the approaches to clinical trials and the development of novel therapeutics in the prior era of population oncology, the current transitional era, and the future era of personalized oncology.

Development of a modeling framework to simulate efficacy endpoints for motesanib in patients with thyroid cancer

PURPOSE

To develop a modeling framework that simulates clinical endpoints (objective response rate and progression-free survival) to support development of motesanib. The framework was evaluated using results from a phase 2 study of motesanib in thyroid cancer.

METHODS

Models of probability and duration of dose modifications and overall survival were developed using data from 93 patients with differentiated thyroid cancer and 91 patients with medullary thyroid cancer, who received motesanib 125 mg once daily. The models, combined with previously developed population pharmacokinetic and tumor growth inhibition models, were assessed in predicting dose intensity, tumor size over time, objective response rate, and progression-free survival. Dose-response simulations were performed in patients with differentiated thyroid cancer.

RESULTS

The predicted objective response rate and median progression-free survival in patients with differentiated thyroid cancer was 15.0% (95% prediction interval, 7.5%-23.7%) and 40 weeks (95% prediction interval, 32-49 weeks), respectively, compared with the observed objective response rate of 14.0% and median progression-free survival of 40 weeks. The simulated median objective response rate increased with motesanib starting dose from 13.5% at 100 mg once daily to 38.0% at 250 mg once daily. However, simulated median progression-free survival was independent of starting dose, ranging from 40.5 weeks (95% prediction interval, 38.6-46.9 weeks) at 100 mg once daily to 40.0 weeks (95% prediction interval, 38.6-46.8 weeks) at 250 mg once daily.

CONCLUSIONS

Dose-response simulations confirmed the appropriateness of 125-mg once-daily dosing; no clinically relevant improvement in progression-free survival would be obtained by dose intensification. This modeling framework represents an important tool to simulate clinical response and support clinical development decisions.

Population pharmacokinetic/pharmacodynamic modeling for the time course of tumor shrinkage by motesanib in thyroid cancer patients

OBJECTIVE

To develop a population pharmacokinetic/pharmacodynamic model describing the relationship between motesanib exposure and tumor response in a phase 2 study of motesanib in patients with advanced differentiated thyroid cancer or medullary thyroid cancer.

METHODS

Data from patients (n = 184) who received motesanib 125 mg once daily were used for population pharmacokinetic/pharmacodynamic modeling. Motesanib concentrations were fitted to a 2-compartment population pharmacokinetic model. Observed change in tumor size was the drug response measure for the pharmacodynamic model. Exposure measures in the pharmacokinetic/pharmacodynamic model included dose, plasma concentration profile, or steady-state area under the concentration versus time curve (AUC( ss )). A longitudinal exposure-tumor response model of drug effect on tumor growth dynamics was used.

RESULTS

Motesanib oral clearance in patients with medullary thyroid cancer was 67% higher than in patients with differentiated thyroid cancer patients (73.7 vs. 44 L/h). Patients' disease type (medullary thyroid cancer vs. differentiated thyroid cancer) was the most important covariate for explaining interpatient variability in clearance. The objective response rates were 14 versus 2% for differentiated thyroid cancer and medullary thyroid cancer, respectively. Motesanib exposure measures (AUC( ss ) or concentration profile) were better predictors of tumor response than motesanib dose. The estimated motesanib concentration yielding tumor stasis (1.9 ng/mL) was lower than the observed trough concentrations in differentiated thyroid cancer and medullary thyroid cancer patients.

CONCLUSIONS

Differences in motesanib pharmacokinetics likely explain the difference in tumor response observed between differentiated thyroid cancer and medullary thyroid cancer patients. The population pharmacokinetic/pharmacodynamic model provides a tool for predicting tumor response to the drug to support the dosing regimen of motesanib in thyroid cancer patients.