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The objectives of the study were to use tumor size data from 10 phase II/III atezolizumab studies across five solid tumor types to estimate tumor growth inhibition (TGI) metrics and assess the impact of TGI metrics and baseline prognostic factors on overall survival (OS) for each tumor type. TGI metrics were estimated from biexponential models and posttreatment longitudinal data of 6699 patients. TGI‐OS full models were built using parametric survival regression by including all significant baseline covariates from the Cox univariate analysis followed by a backward elimination step. The model performance was evaluated for each trial by 1000 simulations of the OS distributions and hazard ratios (HR) of the atezolizumab‐containing arms versus the respective controls. The tumor growth rate estimate was the most significant predictor of OS across all tumor types. Several baseline prognostic factors, such as inflammatory status (C‐reactive protein, albumin, and/or neutrophil‐to‐lymphocyte ratio), tumor burden (sum of longest diameters, number of metastatic sites, and/or presence of liver metastases), Eastern Cooperative Oncology Group performance status, and lactate dehydrogenase were also highly significant across multiple studies in the final multivariate models. TGI‐OS models adequately described the OS distribution. The model‐predicted HRs indicated good model performance across the 10 studies, with observed HRs within the 95% prediction intervals for all study arms versus controls. Multivariate TGI‐OS models developed for different solid tumor types were able to predict treatment effect with various atezolizumab monotherapy or combination regimens and could be used to support design and analysis of future studies.

The time-varying clearance (CL) of the PD-L1 inhibitor atezolizumab was assessed on a population of 1519 cancer patients (primarily with non-small-cell lung cancer or metastatic urothelial carcinoma) from three clinical studies. The first step was to identify the baseline covariates affecting atezolizumab CL without including time-varying components (stationary covariate model). Two time-varying models were then investigated: (1) a model allowing baseline covariates to vary over time (time-varying covariate model), (2) a model with empirical time-varying Emax CL function. The final stationary covariate model included main effects of body weight, albumin levels, tumor size, anti-drug antibodies (ADA) and gender on atezolizumab CL. Both time-varying models resulted in a clear improvement of the data fit and visual predictive checks over the stationary model. The time-varying covariate model provided the best fit of the data. In this model, the main driver for change in CL over time was variations in albumin level with an increase in serum albumin (improvement in a patient’s status) mirroring a decrease in CL. Time-varying ADAs had a small impact (9% increase in CL). None of the covariates impacted atezolizumab CL by more than ± 30% from median. The estimated maximum decrease in CL with time was 22% with the Emax model. The overall impact of covariates on atezolizumab CL did not warrant any change in atezolizumab dosing recommendations. The results support the hypothesis that variation in atezolizumab CL over time is associated with patients’ disease status, as shown with other checkpoint inhibitors.

Summary Long-term use of vismodegib is associated with treatment-emergent adverse drug reactions in clinical trials of patients with locally advanced basal cell carcinoma (laBCC) and metastatic BCC (mBCC). Treatment interruptions have been recommended for management of adverse drug reactions, but there is no clear guidance on the duration of those interruptions. A model-based evaluation was conducted to assess the impact of treatment interruptions on vismodegib efficacy. A tumor growth–inhibition model was developed and model-based simulations were performed to assess the proportion of patients achieving ≥30% reduction from baseline in tumor size (ie, proportion of responders) for various dosing schedules. The most conservative simulated scenario compared an intermittent dosing schedule of 12 weeks of vismodegib treatment followed by 8-week dosing interruption (repeated four times for a total duration of 80 weeks) versus 80 weeks of continuous dosing, which predicted a decrease in responders of –4.7% from 90.7% in laBCC and –3.7% from 63.0% in mBCC. These results demonstrated maintenance of vismodegib efficacy with the intermittent dosing schedule and support the recently approved recommendations of treatment interruptions of up to 8 weeks to manage tolerability in laBCC or mBCC patients in the vismodegib US prescribing information.

Background The iMATRIX-atezolizumab study was a phase I/II, multicenter, open-label study designed to assess the safety and pharmacokinetics of atezolizumab in pediatric and young adult patients. We describe the pharmacokinetics (PK), exposure-safety, and immunogenicity of atezolizumab in pediatric and young adults with metastatic solid tumors or hematologic malignancies enrolled in this study. Methods Patients aged

Purpose To determine the exposure–response (ER) relationships between atezolizumab exposure and efficacy or safety in patients with advanced non-small cell lung cancer (NSCLC) or urothelial carcinoma (UC) and to identify alternative dosing regimens. Methods ER analyses were conducted using pooled NSCLC and UC data from phase 1 and 3 studies (PCD4989g, OAK, IMvigor211; ClinicalTrials.gov IDs, NCT01375842, NCT02008227, and NCT02302807, respectively). Objective response rate, overall survival, and adverse events were evaluated vs pharmacokinetic (PK) metrics. Population PK-simulated exposures for regimens of 840 mg every 2 weeks (q2w) and 1680 mg every 4 weeks (q4w) were compared with the approved regimen of 1200 mg every 3 weeks (q3w) and the maximum assessed dose (MAD; 20 mg/kg q3w). Phase 3 IMpassion130 (NCT02425891) data were used to validate the PK simulations for 840 mg q2w. Observed safety data were evaluated by exposure and body weight subgroups. Results No significant ER relationships were observed for safety or efficacy. Predicted exposures for 840 mg q2w and 1680 mg q4w were comparable to 1200 mg q3w and the MAD and consistent with observed PK data from IMpassion130. Observed safety was similar between patients with a Cmax above and below the predicted Cmax for 1680 mg q4w and between patients in the lowest and upper 3 body weight quartiles. Conclusion Atezolizumab regimens of 840 mg q2w and 1680 mg q4w are expected to have comparable efficacy and safety as the approved regimen of 1200 mg q3w, supporting their interchangeable use and offering patients greater flexibility.

Sildenafil (REVATIO®) was approved for the treatment of adult Pulmonary Arterial Hypertension (PAH) in the US and the EU. A pediatric study has been performed and sildenafil was approved in the EU for pediatric PAH. The long-term extension of this study revealed good survival but also an increased mortality with the high dose of sildenafil compared to lower doses. As a consequence, FDA required Pfizer to evaluate REVATIO®’s effect on the risk of death in adults with PAH. Following FDA’s rationale a survival model was developed to characterize the exposure–mortality relationship and assess its potential impact on an ongoing survival trial in adults in the context of confounding factors. Clinical trial simulations were performed to assess the design of the survival trial in adults (AFFILIATE, NCT02060487), expected to last approximately 8 years according to both assumptions: absence or presence of an exposure–mortality relationship and to quantify the impact of confounding factors on its readout. Simulations showed that the trial would be robust in most conditions. But its interpretation will depend on the number of confounding factors such as additional treatments attempting to control disease progression. Clinical trial identifier NCT00159913 for STARTS-1, NCT00159874 for STARTS-2

Antibody therapeutics can be associated with unwanted immune responses resulting in the development of anti‐drug antibodies (ADA). Optimal methods to evaluate the potential effects of ADA on clinical outcomes in oncology are not well established. In this study, we assessed efficacy and safety, based on ADA status, in patients from over 10 clinical trials that evaluated the immune checkpoint inhibitor atezolizumab as a single agent or as combination therapy for several types of advanced cancers. ADA can only be observed post randomization, and imbalances in baseline prognostic factors can confound the interpretation of ADA impact. We applied methodology to account for the confounding effects of baseline clinical characteristics and survivorship bias on efficacy. Adjusted meta‐analyses revealed that despite numerical differences in overall survival and progression‐free survival between ADA‐positive and ADA‐negative patients from some studies, ADA‐positive patients from studies with an overall treatment effect derived benefit from atezolizumab, compared with their adjusted controls. Based on large, pooled populations from atezolizumab monotherapy or combination studies, unadjusted descriptive analyses did not identify a clear relationship between ADA status and frequency or severity of adverse events. Data also suggested that any ADA impact is not driven by neutralizing activity. Collectively, this exploratory analysis suggests that the potential for ADA development should not impact treatment decisions with atezolizumab.

Baseline patient characteristics and prognostic factors are important considerations in oncology when evaluating the impact of immunogenicity on pharmacokinetics (PK) and efficacy. Here, we assessed the impact of anti‐drug antibodies (ADA) on the PK of the immune checkpoint inhibitor atezolizumab (an anti–PD‐L1 monoclonal antibody). We evaluated data from ≈ 4500 patients from 12 clinical trials across different tumor types, treatment settings, and dosing regimens. In our dataset, ~ 30% of patients (range, 13–54%) developed treatment‐emergent ADA, and in vitro neutralizing antibodies (NAb) were seen in ~ 50% of ADA‐positive (+) patients. Pooled time course data showed a trend toward lower atezolizumab exposure in ADA+ patients, which was more pronounced in ADA+/NAb+ patients. However, the atezolizumab concentration distributions overlapped, and drug concentrations exceeded 6 µg/ml, the target concentration required for receptor saturation, in greater than 95% of patients. Patients had sufficient exposure regardless of ADA status. The dose selected to allow for dosing over effects from ADA resulted in a flat exposure‐response relationship. Analysis of study results by ADA titer showed that exposure and overall survival were not affected in a clinically meaningful way. High tumor burden, low albumin, and high CRP at baseline showed the greatest association with ADA development but not with subsequent NAb development. These imbalanced factors at baseline can confound analysis of ADA impact. ADA increases atezolizumab clearance minimally (9%), and its impact on exposure based on the totality of the clinical pharmacology assessment does not appear to be clinically meaningful.

Introduction: Phase 1b GO30140 and phase 3 IMbrave150 studies evaluated first-line atezolizumab + bevacizumab for unresectable hepatocellular carcinoma (HCC). Here, we evaluated pharmacokinetics (PK) and safety by hepatic impairment status and geographic region. Methods: Patients received atezolizumab 1,200 mg + bevacizumab 15 mg/kg IV every 3 weeks. Drug concentrations were evaluated by descriptive statistics and population PK. PK and adverse event frequencies were evaluated by hepatic impairment status and region. Results: 323 IMbrave150 patients and 162 GO30140 patients were PK evaluable. Compared with IMbrave150 patients who had normal hepatic function per the National Cancer Institute Organ Dysfunction Working Group (NCI-ODWG) criteria (n = 123), patients with mild impairment (n = 171) had a geometric mean ratio (GMR) of 0.92 for cycle 1 atezolizumab area under the concentration-time curve (AUC); patients with moderate impairment (n = 27) had a GMR of 0.88. Patients in Asia ([n = 162] vs. outside [n = 161]) had a GMR of 1.25 for cycle 1 atezolizumab AUC. Compared with GO30140 patients who had normal hepatic function (NCI-ODWG [n = 61]), patients with mild impairment (n = 92) had a GMR of 0.97 for cycle 1 peak bevacizumab concentrations; those with moderate impairment (n = 9) had a GMR of 0.94. Patients in Asia (n = 111) versus outside Asia (n = 51) had a GMR of 0.94 for cycle 1 peak bevacizumab concentration. PK results were generally comparable when evaluated based on additional hepatic functional definitions (Child-Pugh or albumin/bilirubin criteria) or study enrollment in Japan. No associations between atezolizumab PK and HCC etiology were seen. Adverse event frequencies were similar across evaluated groups. Conclusions: IMbrave150 and GO30140 patients with unresectable HCC had varying baseline hepatic impairment and high enrollment from Asia. PK data demonstrated considerable exposure overlap across groups. Treatment was tolerable across groups. No need for dose adjustment based on mild or moderate hepatic impairment or region is recommended based on this analysis.

The aim of this study was to develop a multistate model for overall survival (OS) analysis, based on parametric hazard functions and combined with an investigation of predictors derived from a longitudinal tumor size model on the transition hazards. Different states – stable disease, tumor response, progression, second‐line treatment, and death following docetaxel treatment initiation (stable state) in patients with HER2‐negative breast cancer (n = 183) were used in model building. Past changes in tumor size prospectively predicts the probability of state changes. The hazard of death after progression was lower for subjects who had longer treatment response (i.e., longer time‐to‐progression). Young age increased the probability of receiving second‐line treatment. The developed multistate model adequately described the transitions between different states and jointly the overall event and survival data. The multistate model allows for simultaneous estimation of transition rates along with their tumor model derived metrics. The metrics were evaluated in a prospective manner so not to cause immortal time bias. Investigation of predictors and characterization of the time to develop response, the duration of response, the progression‐free survival, and the OS can be performed in a single multistate modeling exercise. This modeling approach can be applied to other cancer types and therapies to provide a better understanding of efficacy of drug and characterizing different states, thereby facilitating early clinical interventions to improve anticancer therapy.

International audience; Treatment evaluation in advanced cancer mainly relies on overall survival and tumor size dynamics. Both markers and their association can be simultaneously analyzed by using joint models, and these approaches are supported by many softwares or packages. However, these approaches are essentially limited to linear models for the longitudinal part, which limit their biological interpretation.More biological models of tumor dynamics can be obtained by using nonlinear models, but they are limited by the fact that parameter identifiability require rich dataset. In that context Bayesian approaches are particularly suited to incorporate the biological knowledge and increase the information available, but they are limited by the high computing cost of Monte-Carlo by Markov Chains algorithms. Here, we aimed to assess the performances of the Hamiltonian Monte-Carlo (HMC) algorithm implemented in Stan for inference in a nonlinear joint model. The method was validated on simulated data where HMC provided proper posterior distributions and credibility intervalsin a reasonable computational time. Then the association between tumor size dynamics and survival was assessed in patients with advanced or metastatic bladder cancer treated with atezolizumab, an immunotherapy agent. HMC confirmed limited sensitivity to prior distributions. A cross-validation approach was developed and identified the current slope of tumor size dynamics as the most relevant driver of survival. In summary, HMC is an efficient approach to perform nonlinear joint models in a Bayesian framework, and opens the way for the use of nonlinear models to characterize both the rapid dynamics and the intersubject variability observed during cancer immunotherapy treatment.

Dose selection and optimization is an important topic in drug development to maximize treatment benefits for all patients. While exposure-response (E-R) analysis is a useful method to inform dose-selection strategy, in oncology, special considerations for prognostic factors are needed due to their potential to confound the E-R analysis for monoclonal antibodies. The current review focuses on 3 different approaches to mitigate the confounding effects for monoclonal antibodies in oncology: (i) Cox-proportional hazards modelling and case-matching; (ii) tumour growth inhibition-overall survival modelling; and (iii) multiple dose level study design. In the presence of confounding effects, studying multiple dose levels may be required to reveal the true E-R relationship. However, it is impractical for pivotal trials in oncology drug development programmes. Therefore, the strengths and weaknesses of the other 2 approaches are considered, and the favourable utility of tumour growth inhibition-overall survival modelling to address confounding in E-R analyses is described. In the broader scope of oncology drug development, this review discusses the downfall of the current emphasis on E-R analyses using data from single dose level trials and proposes that development programmes be designed to study more dose levels in earlier trials.

We retrospectively investigated the pharmacokinetics and exposure‐efficacy/safety relationships of single‐agent atezolizumab based on tissue tumor mutational burden (tTMB) status (high vs low [≥16 vs, In this work, we show that tissue tumor mutational burden (tTMB) is a biomarker of efficacy with the immune checkpoint inhibitor atezolizumab. The analyses herein support a role for atezolizumab monotherapy in a tumor‐agnostic high‐tTMB population.

Machine learning (ML) was used to leverage tumor growth inhibition (TGI) metrics to characterize the relationship with overall survival (OS) as a novel approach and to compare with traditional TGI-OS modeling methods. Historical dataset from a phase III non-small cell lung cancer study (OAK, atezolizumab vs. docetaxel, N = 668) was used. ML methods support the validity of TGI metrics in predicting OS. With lasso, the best model with TGI metrics outperforms the best model without TGI metrics. Boosting was the best linear ML method for this dataset with reduced estimation bias and lowest Brier score, suggesting better prediction accuracy. Random forest did not outperform linear ML methods despite hyperparameter optimization. Kernel machine was marginally the best nonlinear ML method for this dataset and uncovered nonlinear and interaction effects. Nonlinear ML may improve prediction by capturing nonlinear effects and covariate interactions, but its predictive performance and value need further evaluation with larger datasets.

The purpose of this work is to assess the heterogeneity across organs of response to treatment in metastatic colorectal patient based on longitudinal individual target lesion diameters (ILD) in comparison to sum of tumor lesion diameters (SLD). Data were from the McCAVE trial, in which 189 previously untreated patients with metastatic colorectal carcinoma (mCRC) received either bevacizumab (control, C) or vanucizumab (experimental, E), on top of standard chemotherapy. Bayesian hierarchical longitudinal non-linear mixed effect models were fitted to the data using Hamilton Monte Carlo algorithm to characterize the time dynamics of the tumor burden, and to obtain estimates of the tumor shrinkage and regrowth rates. The ILD model brought more nuanced results than to the SLD model. Besides substantial differences in tumor size at baseline (with lesions located in liver more than twice as large as the ones in lungs), it revealed a more durable response in lesions located in lymph nodes and ‘other organs’ compared to liver and lungs. Specifically, in lymph nodes and ‘other organs’, the projected time to nadir was doubled in group E (2.12 and 2.44 years respectively) compared to group C (1.07 and 1.20 years respectively). This long period of tumor shrinkage associated with a slightly larger change from baseline at nadir (− 51.4% in lymph nodes and − 62.6% in ‘other organs’ in the group E, compared to − 46.2% and − 46.9% in group C) resulted in a clinically meaningful difference in the tumor dynamics of patients in group E compared to the group C. The proportion of variance explained by the inter-lesion variability for each model parameter was large (ranging between 10 and 56%), reflecting the heterogeneity in tumor dynamics across organs. These findings suggest that there is value in understanding both within- and between-patient variability in tumor size’s time dynamics using an appropriate modeling framework, as this information may help in pairing the right treatment with individual patient profile.

The FDA–AACR Oncology Dose-Finding Workshop, Part 3, was held in Washington, DC, on July 20, 2017, as a continuation of the previous two collaborative dose-finding and optimization workshops presented by the FDA and AACR. This year's workshop focused on combination therapy with immune-oncology agents and best practices regarding patient and dose selection, predictive biomarkers, and novel clinical endpoints. This summary highlights viewpoints that emerged during the workshop. Cancer Immunol Res; 5(12); 1058–61. ©2017 AACR.

Roflumilast is a selective phosphodiesterase 4 inhibitor (PDE4i) for the treatment of severe chronic obstructive pulmonary disease (COPD). In 2 large phase 3 trials in a broader population of COPD patients (BY217/M2-111, ClinicalTrials.gov: NCT00076089 and BY217/M2-112, ClinicalTrials.gov: NCT00430729), treatment with roflumilast reduced the rate of exacerbations; however, the reduction did not reach statistical significance. Two linked dose-response models for the primary (annualized COPD exacerbation counts) and secondary (change from baseline in forced expiratory volume in 1 second [FEV1 ]) end points were therefore developed to characterize and quantify effect sizes and the patient characteristics influencing them. The models showed that disease severity and bronchitis, particularly the severity of bronchitis expressed in cough-and-sputum scores, were good predictors of exacerbation rates and differential benefit of roflumilast in exacerbation reduction. The models were used to support the rational design of 2 phase 3 randomized, placebo-controlled clinical trials (BY217/M2-124, ClinicalTrials.gov: NCT00297102 and BY217/M2-125, ClinicalTrials.gov: NCT00297115) by identifying the most appropriate patient population using clinical trial simulations. Model predictions for both end points were found to be highly accurate - as confirmed by the results from these trials, which led to the approval of roflumilast as the first oral PDE4i for the treatment of COPD in patients associated with chronic bronchitis and a history of exacerbations.

There is a need for new approaches and endpoints in oncology drug development, particularly with the advent of immunotherapies and the multiple drug combinations under investigation. Tumor dynamics modeling, a key component to oncology “model-informed drug development,” has shown a growing number of applications and a broader adoption by drug developers and regulatory agencies in the past years to support drug development and approval in a variety of ways. Tumor dynamics modeling is also being investigated in personalized cancer therapy approaches. These models and applications are reviewed and discussed, as well as the limitations and issues open for further investigations. A close collaboration between stakeholders like clinical investigators, statisticians, and pharmacometricians is warranted to advance clinical cancer therapeutics.

The large heterogeneity in response to immune checkpoint inhibitors is driving the exploration of predictive biomarkers to identify patients who will respond to such treatment. We extended our previously suggested modeling framework of atezolizumab pharmacokinetics, IL18, and tumor size (TS) dynamics, to also include overall survival (OS). Baseline and model‐derived variables were explored as predictors of OS in 88 patients with non‐small cell lung cancer treated with atezolizumab. To investigate the impact of follow‐up length on the inclusion of predictors of OS, four different censoring strategies were applied. The time‐course of TS change was the most significant predictor in all scenarios, whereas IL18 was not significant. Identified predictors of OS were similar regardless of censoring strategy, although OS was underpredicted when patients were censored 5 months after last dose. The study demonstrated that the tumor‐time course‐OS relationship could be identified based on early phase I data.

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 magnitude of interindividual pharmacokinetic variability (IIV) of a drug and the factors responsible for this variability are intensively studied before-and sometimes after-registration as crucial information in anticipating and understanding variability in toxicity and efficacy. However, there has been much less attention paid to intraindividual variability, reflecting random or systematic changes in an individual's pharmacokinetics over time. We have chosen to focus on small-molecule kinase inhibitors (SMKIs).

Elders and Indigenous land users in the Peace-Athabasca Delta (PAD) have observed a dramatic decline in the relative abundance of muskrat in recent decades (~1935–2014). The main explanation for the decline has been reduction in suitable habitat as a result of decades with reduced frequency of ice-jam flooding on the Peace River. Under favourable conditions, ice jams can cause flooding of perched basins within the PAD that would otherwise receive no recharge from floodwaters. To examine whether abundance of muskrat in the PAD is driven by flooding, we tested the predictions that the density of muskrat (estimated by winter counts of houses) (1) was inversely related to the number of years since major ice jam floods and (2) increased with water depth. An ongoing collaborative monitoring program initiated in 2011, combined with analysis of data from past surveys (1973–2015), allowed Indigenous land users and scientists to document a 10 to 100-fold increase in the density of muskrat houses in 24 basins, over the two years following ice-jam flood events in the PAD. During 1973–2015, in the periods between major floods, density of houses dropped by approximately 79% for every year after a significant flood. In 27 basins surveyed from 2011 to 2015, density of muskrat houses increased by two orders of magnitude in the two years following a flood in the spring of 2014. Density of muskrat houses had a non-linear relationship with estimated depth of water at the time of fall freeze-up; the highest densities of muskrat houses were in basins with about 60 – 250 cm of water at the time of freeze-up. The depth of snow at the time of surveys did not have a strong relationship with the density of muskrat houses. However, few houses were counted in basins with more than 20 cm of snow, likely because deeper snow made it more difficult to conduct surveys and spot houses. Factors other than an increase in the depth of water at fall freeze-up may provide the mechanisms by which flooding affects muskrat. Density of muskrat houses is clearly tied to ice-jam flooding in the PAD. However, the local mechanisms by which floods affect muskrat are best understood by Indigenous land users and remain poorly understood by Western science. Indigenous peoples continue to regard muskrat as an indicator of ecological and cultural health of the PAD. This study highlights the value of consistent ecological monitoring that includes Indigenous knowledge.

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.

To assess the link between tumor growth inhibition (TGI) and overall survival (OS) based on historical renal cell carcinoma (RCC) data. To illustrate how simulations can help to identify TGI thresholds based on target OS benefit [i.e., hazard ratio (HR) compared with standard of care] to support new drug development in RCC. Tumor size (TS) data were modeled from 2552 patients with first-line or refractory RCC who received temsirolimus, interferon, sunitinib, sorafenib or axitinib in 10 Phase II or Phase III studies. Three model-based TGI metrics estimates [early tumor shrinkage (ETS) at week 8, 10 or 12, time to tumor growth (TTG) and growth rate] as well as baseline prognostic factors were tested in multivariate lognormal models of OS. Model performance was evaluated by posterior predictive check of the OS distributions and hazard ratio across treatments. TTG was the best TGI metric to predict OS. However, week 8 ETS had a satisfactory performance and was employed in order to maximize clinical utilization. The week 8 ETS to OS model was then used to simulate clinically relevant ETS thresholds for future Phase II studies with investigational treatments. The published OS model and resultant simulations can be leveraged to support Phase II design and predict expected OS and HR (based on early observed TGI data obtained in Phase II or Phase III studies), thereby informing important mRCC development decisions, e.g., Go/No Go and dose regimen selection.

To compare lesion-level and volumetric measures of tumor burden with sum of the longest dimensions (SLD) of target lesions on overall survival (OS) predictions using time-to-growth (TTG) as predictor.Tumor burden and OS data from a phase 3 randomized study of second-line FOLFIRI ± aflibercept in metastatic colorectal cancer were available for 918 patients out of 1216 treated (75%). A TGI model that estimates TTG was fit to the longitudinal tumor size data (nonlinear mixed effect modeling) to estimate TTG with: SLD, sum of the measured lesion volumes (SV), individual lesion diameters (ILD), or individual lesion volumes (ILV). A parametric OS model was built with TTG estimates and assessed for prediction of the hazard ratio (HR) for survival.Individual lesions had consistent dynamics within individuals. Between-lesion variability in rate constants was lower (typically 27% CV) than inter-patient variability (typically 50% CV). Estimates of TTG were consistent (around 12 weeks) across tumor size assessments. TTG was highly significant in a log-logistic parametric model of OS (median over 12 months). When individual lesions were considered, TTG of the fastest progressing lesions best predicted OS. TTG obtained from the lesion-level analyses were slightly better predictors of OS than estimates from the sums, with ILV marginally better than ILD. All models predicted VELOUR HR equally well and all predicted study success.This analysis revealed consistent TGI profiles across all tumor size assessments considered. TTG predicted VELOUR HR when based on any of the tumor size measures.

Purpose: Standard endpoints often poorly predict overall survival (OS) with immunotherapies. We investigated the predictive performance of model-based tumor growth inhibition (TGI) metrics using data from atezolizumab clinical trials in patients with non–small cell lung cancer. Patients and Methods: OS benefit with atezolizumab versus docetaxel was observed in both POPLAR (phase II) and OAK (phase III), although progression-free survival was similar between arms. A multivariate model linking baseline patient characteristics and on-treatment tumor growth rate constant (KG), estimated using time profiles of sum of longest diameters (RECIST 1.1) to OS, was developed using POPLAR data. The model was evaluated to predict OAK outcome based on estimated KG at TGI data cutoffs ranging from 10 to 122 weeks. Results: In POPLAR, TGI profiles in both arms crossed at 25 weeks, with more shrinkage with docetaxel and slower KG with atezolizumab. A log-normal OS model, with albumin and number of metastatic sites as independent prognostic factors and estimated KG, predicted OS HR in subpopulations of patients with varying baseline PD-L1 expression in both POPLAR and OAK: model-predicted OAK HR (95% prediction interval), 0.73 (0.63–0.85), versus 0.73 observed. The POPLAR OS model predicted greater than 97% chance of success of OAK (significant OS HR, P < 0.05) from the 40-week data cutoff onward with 50% of the total number of tumor assessments when a successful study was predicted from 70 weeks onward based on observed OS. Conclusions: KG has potential as a model-based early endpoint to inform decisions in cancer immunotherapy studies. Clin Cancer Res; 24(14); 3292–8. ©2018 AACR.

Introduction Pain is a major symptom in many medical conditions which can be relieved thanks to analgesics. The goal of this work was to present an indirect comparison of efficacy and tolerability profiles of two analgesics, tramadol and tapentadol, in patients with chronic non-malignant pain. Methods In the absence of a head-to-head comparison between these two opioid drugs, model-based meta-analyses were used to characterize the pain intensity time dynamics and evaluate the proportions of most frequent adverse events (constipation, nausea, vomiting, dizziness, and somnolence) and drop-outs (due to adverse event, as well as due to lack of efficacy) in each treatment group. Using these models, the investigational treatments were compared on the basis of Monte Carlo simulation outcomes. Results Data were extracted from 45 Phase II and Phase III studies representing a total of 81 treatment arms, i.e., approximately 13,000 patients. The pain intensity model shows, that after having adjusted for differences in baseline pain intensity and placebo effects, tramadol 300 mg once daily (qd) was slightly more effective in reducing pain than tapentadol 100–250 mg twice daily (bid), with a 46% change from baseline for the former versus 36% for the latter. From a tolerability standpoint, both drugs showed, as expected, increased risks of adverse events compared to placebo. Yet, tapentadol was associated with slightly lower risks of constipation, and nausea than tramadol. Conclusion Overall, the analysis showed that the benefit–risk profiles of tramadol 300 mg qd and tapentadol 100–250 mg bid were approximately even. The amount of data to characterize dose–response relationships was sufficient only in the tramadol group; public access to tapentadol efficacy and tolerability readouts across a wide dose range in chronic non-malignant pain would allow a comparison of therapeutic indices, a straight quantitation of the benefit–risk ratio. Knowing that their side-effects have been identified as potential hindrance to prescription, a broad and open access to clinical trial data in this indication is encouraged in order to facilitate the evaluation of the opiate analgesics clinical utility. Electronic supplementary material The online version of this article (doi:10.1007/s40122-014-0023-5) contains supplementary material, which is available to authorized users.

Model-based drug development in oncology is still lagging despite a good momentum in the clinical pharmacology and pharmacometry community in the past few years. The failure rate of late-stage oncology studies is one of the highest across therapeutic areas. The modeling of the relationship between longitudinal tumor size and overall survival has been proposed to enhance learning in early clinical studies, to predict overall survival, and to simulate clinical trials. This approach has the potential to support proof of concept, early clinical decisions, and design of late-stage trials, but it is not yet widely integrated into the oncology drug development process. In this article, we review the state of these modeling efforts and discuss several key applications of these models. We conclude by suggesting a few paths forward.

Atezolizumab, a humanized immunoglobulin G1 (IgG1) monoclonal antibody targeting human programmed death-ligand 1 (PD-L1), is US Food and Drug Administration (FDA) approved in metastatic urothelial carcinoma (MUC) and is being investigated in various malignancies. This analysis based upon 906 patients from two phase I and one phase II MUC studies, is the first report of the clinical pharmacokinetics (PK) and pharmacodynamics (PD) of atezolizumab. Atezolizumab exhibited linear PK over a dose range of 1-20 mg/kg, including the labeled 1,200 mg dose. The clearance, volume of distribution, and terminal half-life estimates from population pharmacokinetic (PopPK) analysis of 0.200 L/day, 6.91 L, and 27 days, respectively, were as expected for an IgG1. Exposure-response analyses did not identify statistically significant relationships with either objective response rate or adverse events of grades 3-5 or of special interest. None of the statistically significant covariates from PopPK (body weight, gender, antitherapeutic antibody, albumin, and tumor burden) would require dose adjustment.

The phase III trial comparing onartuzumab + erlotinib vs. erlotinib in the second- and third-line non-small cell lung cancer (NSCLC) did not meet its primary endpoint of overall survival (OS). The objective was to assess whether doses higher than the phase III dose (15 mg/kg) might yield better efficacy without compromising the safety profile. Data were from 636 patients from the phase II and III NSCLC studies. Tumor growth inhibition (TGI) models were fit to longitudinal tumor size data to estimate individual TGI metrics including time to tumor re-growth (TTG). Cox regression models were developed for time-to-event endpoints (progression-free survival (PFS), OS, and TTG) to investigate relationships with baseline prognostic factors and onartuzumab exposure. Incidence of adverse events was modeled by logistic regression. In the final models, higher onartuzumab exposure was associated with longer PFS, but not with longer OS. Longer OS was associated with higher baseline albumin, longer TTG, smaller number of metastatic sites, female gender, lower ECOG score, and younger age. TTG was the only TGI metric retained in the final OS model. Onartuzumab exposure was not significantly associated with TTG after adjusting for prognostic factors. Higher Cmin was associated with increased incidence of infusion reactions and peripheral edema. Higher onartuzumab exposure was not significantly associated with improved OS after adjusting for prognostic factors and TTG, and there was a trend of unknown clinical significance toward increased incidence of infusion reactions and peripheral edema. These results did not support testing higher onartuzumab doses.

To assess the link between early tumor shrinkage (ETS) and progression-free survival (PFS) based on historical first-line metastatic renal cell carcinoma (mRCC) data. Tumor size data from 921 patients with first-line mRCC who received interferon-alpha, sunitinib, sorafenib or axitinib in two Phase III studies were modeled. The relationship between model-based estimates of ETS at week 8 as well as the baseline prognostic factors and PFS was tested in multivariate log-logistic models. Model performance was evaluated using simulations of PFS distributions and hazard ratio (HR) across treatments for the two studies. In addition, an external validation was conducted using data from an independent Phase II RCC study. The relationship between expected HR of an investigational treatment vs. sunitinib and the differences in ETS was simulated. A model with a nonlinear ETS-PFS link was qualified to predict PFS distribution by ETS quartiles as well as to predict HRs of sunitinib vs. interferon-alpha and axitinib vs. sorafenib. The model also performed well in simulations of an independent study of axitinib (external validation). The simulations suggested that if a new investigational treatment could further reduce the week 8 ETS by 30 % compared with sunitinib, an expected HR [95 % predictive interval] of the new treatment vs. sunitinib would be 0.59 [0.46, 0.79]. A model has been developed that uses early changes in tumor size to predict the HR for PFS differences between treatment arms for first-line mRCC. Such a model may have utility in predicting the outcome of ongoing studies (e.g., as part of interim futility analyses), supporting early decision making and future study design for investigational agents in development for this indication.

Most of the cancer chemotherapy treatments employ drugs in combination. For combination treatments, it is relevant to assess interaction between two or more anticancer agents used in clinics. Based on clinical data and using modeling techniques, the work analyzes the pharmacodynamic interaction between capecitabine and docetaxel used in combination in metastatic breast cancer.We developed mathematical models to describe tumor growth inhibition profile under treatment based on Phase II and Phase III clinical data of capecitabine and docetaxel in metastatic breast cancer. Model parameters were estimated by population approach with NONMEM(®) on single-agent and combination data. Simulations were performed using MATLAB.Capecitabine and docetaxel combination in metastatic breast cancer results in a synergistic effect as compared with the simple additive effects of single-agent treatments. Docetaxel is more efficient than capecitabine at the start of treatment but develops resistance faster. Modeling revealed no resistance of capecitabine for the combination data.Modeling could be a powerful tool to design the most advantageous combination regimen for capecitabine and docetaxel in metastatic breast cancer in order to increase the time before regrowth and decrease the tumor size at regrowth.

62 Background: Standard early endpoints are often poor predictors of overall survival (OS) for immunotherapies. There is a need for new intermediate endpoints to support upcoming combination studies with immune checkpoint inhibitors. We investigated the predictive performance of on-treatment tumor growth rate (TGR) estimates to predict OS in atezolizumab clinical trials in patients (pts) with metastatic urinary carcinoma (mUC). Methods: Atezolizumab showed durable response, prolonged OS and good tolerability supporting its therapeutic use in mUC (Rosenberg, Lancet 2016; Balar, lancet 2017; Powles, EACR-AACR-SIC 2017). A multivariate model linking baseline patient characteristics and TGR (estimated using time profile of sum of longest diameters (SLD) of target lesions, RECIST 1.1) to OS was developed using phase 2 data (IMvigor210) and validated to predict phase 3 trial (IMvigor211). Results: To estimate TGR, patients needed to have at least one post-baseline measurement: n = 365/429 (85%) in IMvigor210 (102 in cohort 1, 1L cisplatin-ineligible and 263 in cohort 2, prior platinum-treated 2L+) and n = 745/902 treated (83%) in IMvigor211 (prior platinum-treated 2/3L). A lognormal model with independent baseline prognostic factors (alkaline phosphatase, ECOG performance status and number of metastatic sites) and estimated TGR was developed. The model predicted OS distributions in the two IMvigor210 cohorts in all comers as well as by PD-L1 expression on tumor-infiltrating immune cells (IC). In IMvigor211, SLD profiles in atezolizumab and chemotherapy arms crossed at about 40 weeks with more initial shrinkage in chemotherapy-treated pts but slower overall TGR in atezolizumab-treated pts. The IMvigor210 OS model predicted OS hazard ratio (HR) in IMvigor211 in all comers with post-baseline tumor scans (model-predicted HR [prediction interval]: 0.80 [0.70-0.92] vs. 0.74 observed) as well as by IC status: 0.66 [0.50-0.87] vs. 0.70 observed in IC23. Conclusions: Model-based TGR is a promising exploratory endpoint and is being investigated to support early decisions in signal-seeking atezolizumab combination trials in mUC.

To characterize the population pharmacokinetics of bevacizumab and the influence of demographic factors, disease severity, and concomitantly used chemotherapy agents on it's pharmacokinetic behavior.Data from eight clinical trials with bevacizumab administered by intravenous infusion were included. A total of 4,629 bevacizumab concentrations from 491 patients with solid tumors, who received bevacizumab doses ranging from 1 to 20 mg/kg at a dosing frequency ranging from weekly to every 3 weeks, were analyzed using a nonlinear mixed-effects modeling approach (NONMEM).The best structural model was a two-compartment model with first-order elimination. In the final model, estimated clearance (CL) and central compartment volume of distribution (Vc) were 0.207 L/day and 2.39 L for a typical female. The terminal half-life estimate was approximately 20 days for both men and women. Body weight and gender were the most significant covariates to explain interpatient variability for CL and Vc. Clearance was 26% faster in men than in women. Patients with low serum albumin and high serum alkaline phosphatase had 19 and 23% faster CL, respectively, than a typical patient. Consistent with the long elimination half life, simulations showed that similar steady-state exposures can be maintained when the weekly mg/kg dose rate is maintained, therefore allowing administration of bevacizumab to coincide with the frequency of administration of the cytotoxic agents.The PK parameters were consistent with those of other IgG molecules. The results support dosing bevacizumab on a once every 2 weeks or once every 3 weeks dosing schedule on a mg/kg basis.

To characterize cobimetinib pharmacokinetics and evaluate impact of clinically relevant covariates on cobimetinib pharmacokinetics. Plasma samples (N = 4886) were collected from 487 patients with various solid tumors (mainly melanoma) in three clinical studies (MEK4592g, NO25395, GO28141). Cobimetinib was administered orally, once daily on either a 21-day-on/7-day-off, 14-day-on/14-day-off or 28-day-on schedule in a 28-day dosing cycle as single agent or in combination with vemurafenib. Cobimetinib doses ranged from 2.1 to 125 mg. NONMEM was used for pharmacokinetic analysis. A linear two-compartment model with first-order absorption, lag time and first-order elimination described cobimetinib pharmacokinetics. The typical estimates (inter-individual variability) of apparent clearance (CL/F), central volume of distribution (V2/F) and terminal half-life were 322 L/day (58 %), 511 L (49 %) and 2.2 days, respectively. Inter-occasion variability on relative bioavailability was estimated at 46 %. CL/F decreased with age. V2/F increased with body weight (BWT). However, the impact of age and BWT on cobimetinib steady-state exposure (peak and trough concentrations and AUC following the recommended daily dose of 60 mg 21-day-on/7-day-off) was limited (

Objective Our objective was to assess the pharmacokinetics of erlotinib in a large patient population with solid tumors, identify covariates, and explore relationships between exposure and safety outcomes (rash and diarrhea) in patients with non-small cell lung cancer receiving single-agent erlotinib. Methods The population pharmacokinetic analysis was performed by use of NONMEM based on 4068 concentration samples from 1047 patients receiving erlotinib as a single agent or in combination with chemotherapy. By use of a 1-compartment model with first-order absorption, the influence of demographic and clinical characteristics on clearance and volume was examined. Spearman rank correlation analyses were performed to test for correlations between maximum grades of rash and diarrhea and erlotinib exposure in non-small cell lung cancer patients treated with single-agent erlotinib. Results On the basis of the final model developed from patients treated with erlotinib as a single agent, the oral clearance was 3.95 L/h, the oral volume of distribution was 233 L, and the absorption rate was 0.95 h−1. The median erlotinib half-life based on this patient population was 36.2 hours. Total bilirubin, α1-acid glycoprotein, and smoking status were the most important factors affecting clearance. The clearance in current smokers was 24% faster than that in former smokers or those who never smoked. There was a statistically significant correlation between drug exposure and rash (P < .05). However, there was significant overlap in the range of values for patients who had no rash (grade = 0) and those who had any grade of rash. No significant correlation was found between exposure and diarrhea. Conclusions The long half-life of erlotinib supports the current once-daily dosing regimen at 150 mg/d. Effects of covariates on erlotinib clearance and correlations with adverse event severity were provided to aid in the detection of a treatment-emergent effect. Clinical Pharmacology & Therapeutics (2006) 80, 136–145; doi: 10.1016/j.clpt.2006.04.007

The population pharmacokinetics of efalizumab was characterized in patients with moderate to severe plaque psoriasis. The study included 1088 subjects who received 1 or 2 mg/kg/wk subcutaneous efalizumab for 12 weeks from a phase I (64 subjects) and 3 phase III studies with day 42 and/or day 84 trough levels (1024 patients). Due to the limitation of the data, a 1-compartment model with first-order absorption and elimination was used to fit the data. The population means for V/F, Ka, and CL/F were 9.13 L, 0.191 day(-1), and 1.29 L/d, respectively, for a typical subject receiving a 1-mg/kg dose. Interindividual variability in CL/F was 48.2%. Body weight has the largest influence on CL/F. Other covariates (obesity, baseline lymphocyte counts, Psoriasis Area and Severity Index score, and age) had only modest effects. Subjects in the 2-mg/kg dose group had a 24.0% lower CL/F, consistent with nonlinear pharmacokinetics of efalizumab. The results of this analysis support the current body weight-adjusted dosing strategy.

Model-based estimates of tumor growth inhibition (TGI) metrics have the potential to enhance learning in early (phase II) clinical studies. They can be used as end points and biomarkers to predict treatment effect on clinical outcome measures—e.g., overall survival (OS)—and support phase II study design, end-of-phase II decisions, and phase III planning and execution. Efforts should be made to assess models in simulating independent studies with treatments of varying mechanisms of action. Clinical Pharmacology & Therapeutics (2013); 93 4, 303–305. doi:10.1038/clpt.2013.8

Aims A major concern with any antithrombotic therapy is an increase in the risk of haemorrhage. The aim of this study was to analyse population pharmacokinetics and pharmacokinetic/pharmacodynamic (PK/PD) relationships for enoxaparin in patients with unstable angina (UA) and non-ST-segment elevation myocardial infarction (NSTEMI), which may help predict risk of haemorrhage. Methods Anti-factor Xa (anti-Xa) activity was measured as marker of enoxaparin concentration in 448 patients receiving the drug as a single 30-mg intravenous bolus followed by 1.0 or 1.25 mg kg−1 subcutaneously twice a day. A population pharmacokinetic analysis was conducted and individual estimates of enoxaparin clearance and area under the curve were tested as prognostic factors for the occurrence of haemorrhagic episodes. Results Basic population PK parameters were an enoxaparin clearance of 0.733 l h−1[95% confidence interval (CI) 0.698, 0.738], a distribution volume of 5.24 l (95% CI 4.20, 6.28) and an elimination half-life of 5.0 h. Enoxaparin clearance was significantly related to patient weight and creatinine clearance, and was the only independent predictor of experiencing both all (10.7%, P = 0.0013) and major (2.2%, P = 0.0004) haemorrhagic events. A creatinine clearance of 30 ml min−1 was associated with a decrease in enoxaparin clearance of 27% compared with that in a patient with a median creatinine clearance of 88 ml min−1, and was related to a 1.5- and 3.8-fold increase in the risk of ‘all’ and ‘major’ haemorrhagic episodes, respectively. Conclusions Enoxaparin clearance depends on body weight, and, therefore, weight-adjusted dosing is recommended to minimize interpatient variability in drug exposure and the risk of haemorrhage. The importance of an increased risk of haemorrhage with decreasing renal function must be weighed against the benefit of treatment with enoxaparin in patients with UA and NSTEMI.

e14517 Background: Standard early endpoints (ORR, PFS) are often poor predictors of overall survival (OS) for immunotherapies. Model-based tumor kinetic (TUK) metrics may offer an alternative (Claret, JCO 2009 and 2013). We investigated the predictive performance of TUK metrics using early data from atezolizumab clinical trials in patients (pts) with non-small cell lung cancer (NSCLC). Methods: OS benefit with atezolizumab (atezo) vs. docetaxel (doc) was observed in both Phase II POPLAR (hazard ratio 0.73, p = 0.040, n = 297, Fehrenbacher Lancet 2016) and Phase III OAK (HR = 0.73, p = 0.0003, n = 850, Rittmeyer Lancet 2016) studies although PFS was similar between arms. A multivariate model linking baseline patient characteristics and tumor growth rate (TGR, estimated using time profile of sum of longest diameters of target lesions, RECIST 1.1) to OS was developed using POPLAR data and qualified to predict outcome by PD-L1 status in both POPLAR and OAK. This model was used to predict OAK OS outcome based on patient characteristics and estimated TGR from TUK data at several OAK data cut-offs ranging from 10 to 122 weeks. Results: In POPLAR, TUK profile in atezo and doc arms crossed at about 25 weeks with more initial shrinkage in doc-treated patients and slower TGR in atezo-treated patients. A lognormal OS model with albumin and number of metastatic sites as independent baseline prognostic factors and estimated TGR predicted OS HR in sub-populations of pts based on baseline PD-L1 expression both in POPLAR as well as in OAK e.g model-predicted OAK HR: 0.73 [0.63-0.85]. Based a retrospective analysis on TUK data cuts, the POPLAR OS model predicted significant OS HR in OAK as soon as 40 week cut-off: HR: 0.67 [0.56-0.80] with success in 99% of the replicates with only 50% of the total number of tumor assessments. At this 40 week data cut, immature observed OS HR was 0.84 [0.59-1.21, p = 0.347] and HR only reached significance at 70 weeks. Conclusions: Model predictions based on TUK can be used to predict success early before maturation of OS data as shown with OAK. The model-based biomarker TUK metric TGR has strong potential as an alternative early endpoint to evaluate efficacy in cancer immunotherapy studies.