Randomized Phase III Study of Amcenestrant Plus Palbociclib Versus Letrozole Plus Palbociclib in Estrogen Receptor-Positive, Human Epidermal Growth Factor Receptor 2-Negative Advanced Breast Cancer: Primary Results From AMEERA-5.

PURPOSE: AMEERA-5 investigated amcenestrant (oral selective estrogen receptor [ER] degrader) plus palbociclib versus letrozole plus palbociclib as first-line treatment for ER-positive/human epidermal growth factor receptor 2-negative (ER+/HER2-) advanced/metastatic breast cancer (aBC). MATERIALS AND METHODS: In AMEERA-5 (ClinicalTrials.gov identifier: NCT04478266), a double-blind, double-dummy, international phase III trial, adult pre-/post-menopausal women and men without previous systemic therapy for ER+/HER2- aBC were randomly assigned 1:1 to amcenestrant 200 mg once daily + standard palbociclib dosage (125 mg once daily, 21 days on/7 days off) or letrozole 2.5 mg once daily + standard palbociclib dosage, stratified by de novo metastatic disease, postmenopausal women, and visceral metastasis. The primary end point was progression-free survival (PFS), compared using a stratified log-rank test with one-sided type I error rate of 2.5%. Secondary end points included overall survival (key secondary), pharmacokinetics, and safety. RESULTS: Between October 14, 2020, and December 2, 2021, 1,068 patients were randomly assigned to amcenestrant + palbociclib (N = 534) or letrozole + palbociclib (N = 534). At the interim analysis (median follow-up 8.4 months), the stratified hazard ratio for PFS was 1.209 (95% CI, 0.939 to 1.557; one-sided P value = .9304); therefore, the study was stopped for futility. The 6-month PFS rate was 82.7% (95% CI, 79.0 to 85.8) with amcenestrant + palbociclib versus 86.9% (95% CI, 83.5 to 89.6) with letrozole + palbociclib. In the amcenestrant + palbociclib versus letrozole + palbociclib groups, treatment-emergent adverse events (any grade) occurred in 85.6% versus 85.4% of patients and grade ≥3 events in 46.3% versus 60.8%, respectively. CONCLUSION: The AMEERA-5 study was discontinued on the basis of the recommendation of the data monitoring committee at the interim futility analysis. No new safety signals were identified.

An Overview of Current Methods for Real-world Applications to Generalize or Transport Clinical Trial Findings to Target Populations of Interest.

It has been well established that randomized clinical trials have poor external validity, resulting in findings that may not apply to relevant-or target-populations. When the trial is sampled from the target population, generalizability methods have been proposed to address the applicability of trial findings to target populations. When the trial sample and target populations are distinct, transportability methods may be applied for this purpose. However, generalizability and transportability studies present challenges, particularly around the strength of their conclusions. We review and summarize state-of-the-art methods for translating trial findings to target populations. We additionally provide a novel step-by-step guide to address these challenges, illustrating principles through a published case study. When conducted with rigor, generalizability and transportability studies can play an integral role in regulatory decisions by providing key real-world evidence.

Empirical bayes approach for dynamic bayesian borrowing for clinical trials in rare diseases.

Application of Bayesian methods is one the tools that can be used to face the multiple challenges that are met when clinical trials must be conducted in rare diseases. We propose in this work to use a dynamic Bayesian borrowing approach, based on a mixture prior, to complement the control arm of a comparative trial and estimate the mixture parameter by an Empirical Bayes approach. The method is compared, using simulations, with an approach based on a pre-specified (non-adaptive) informative prior. The simulation study shows that the proposed method exhibits similar power as the non-adaptive prior and drastically reduce type I error in case of severe discrepancy between the informative prior and the study control arm data. In case of limited discrepancy between the informative prior and the study control arm data, then our proposed adaptive prior does not reduce the inflation of the type I error.

Use of pharmacodynamic modeling for Bayesian information borrowing in pediatric clinical trials.

The use of Bayesian methodology to design and analyze pediatric efficacy trials is one of the possible options to reduce their sample size. This reduction of the sample size results from the use of an informative prior for the parameters of interest. In most of the applications, the principle of 'information borrowing' from adults' trials is applied, which means that the informative prior is constructed using efficacy results in adult of the drug under investigation. This implicitly assumes similarity in efficacy between the selected pediatric dose and the efficacious dose in adults. The goal of this article is to propose a method to construct prior distribution for the parameter of interest, not directly constructed from the efficacy results of the efficacious dose in adult patients but using pharmacodynamic modeling of a bridging biomarker using early phase pediatric data. When combined with a model bridging the biomarker with the clinical endpoints, the prior is constructed using a variational method after simulation of the parameters of interest. A use case application illustrates how the method can be used to construct a realistic informative prior.

Development and validation of a predictive model to guide the use of plerixafor in pediatric population.

Plerixafor, a CXCR4 receptor antagonist, reduces the binding and chemotaxis of hematopoietic stem cells to the bone marrow stroma, resulting in predictable peak of cluster of differentiation 34+ (CD34+) cells in the peripheral blood (PB) approximately 10 h after its administration. We developed a model that could predict the CD34+ harvest volume on the first day of apheresis (AP-CD34+) based on PB-CD34+ counts immediately prior to commencing apheresis in pediatric population. In all, data from 45 pediatric patients from the MOZAIC study who received either granulocyte colony-stimulating factor (G-CSF) alone or G-CSF plus plerixafor were included. The modeling of the data exhibited a strong and highly predictive linear relationship between the counts of PB-CD34+ cells on the first day of apheresis and AP-CD34+ cells collected on the same day. It is predicted that there are approximately 13 new collected CD34+ cells for 100 new circulating CD34+ cells before apheresis. Our predictive algorithm can be used to quantify the minimal count of PB-CD34+ cells that enables to collect at least 2 × 106 or 5 × 106 AP-CD34+ cells/kg with sufficient assurance (probability = 0.90) and can guide the use of plerixafor in patients at higher perceived risk for mobilization failure. Trial registration of MOZAIC study: ClinicalTrials.gov, NCT01288573; EudraCT, 2010-019340-40.

Exposure-response analyses for selection/confirmation of optimal isatuximab dosing regimen in combination with pomalidomide/dexamethasone treatment in patients with multiple myeloma.

Isatuximab is an approved anti-CD38 monoclonal antibody with multiple antitumor modes of action. An exposure-response (E-R) analysis using data from patients with relapsed/refractory multiple myeloma (RRMM) enrolled in a phase Ib clinical study who received isatuximab at doses from 5 to 20 mg/kg weekly for 1 cycle (4 weeks) followed by every 2 weeks thereafter (qw/q2w) in combination with pomalidomide/dexamethasone (n = 44) was first used to determine the optimal dose/schedule for the phase III ICARIA-MM study. It was complemented by an E-R analysis from a second phase Ib study of patients who received isatuximab at doses from 3 to 10 mg/kg q2w or 10 or 20 mg/kg qw/q2w in combination with lenalidomide/dexamethasone (n = 52). Plasma trough concentration at week 4 (CT4W) was the best predictor for response, and the benefit of the initial 4-weekly administration was confirmed. Although the predicted overall response rate (ORR) was higher at 20 mg/kg vs. 10 mg/kg, the 95% confidence intervals were overlapping. Considering the high probability of success to reach the targeted ORR of greater than or equal to 60%, 10 mg/kg qw/q2w was selected. Results of the E-R analysis from the lenalidomide/dexamethasone study and published disease modeling using data from both phase Ib clinical studies reinforced 10 mg/kg qw/q2w as the optimal dose/schedule for the phase III ICARIA-MM study. E-R analysis showed that higher CT4W was associated with higher ORR. Developed models supported the phase III isatuximab dosing regimen selection/confirmation of 10 mg/kg qw/q2w for use in combination with pomalidomide/dexamethasone in patients with RRMM.

Joint modelling and simulation of M-protein dynamics and progression-free survival for alternative isatuximab dosing with pomalidomide/dexamethasone.

AIMS: Addition of isatuximab (Isa) to pomalidomide/dexamethasone (Pd) significantly improved progression-free survival (PFS) in patients with relapsed/refractory multiple myeloma (RRMM). We aimed to characterize the relationship between serum M-protein kinetics and PFS in the phase 3 ICARIA-MM trial (NCT02990338), and to evaluate an alternative dosing regimen of Isa by simulation. METHODS: Data from the ICARIA-MM trial comparing Isa 10 mg/kg weekly for 4 weeks then every 2 weeks (QW-Q2W) in combination with Pd versus Pd in 256 evaluable RRMM patients were used. A joint model of serum M-protein dynamics and PFS was developed. Trial simulations were then performed to evaluate whether efficacy is maintained after switching to a monthly dosing regimen. RESULTS: The model identified instantaneous changes (slope) in serum M-protein as the best on-treatment predictor for PFS and baseline patient characteristics impacting serum M-protein kinetics (albumin and ß2-microglobulin on baseline levels, non-IgG type on growth rate) and PFS (presence of plasmacytomas). Trial simulations demonstrated that switching to a monthly Isa regimen at 6 months would shorten median PFS by 2.3 weeks and induce 42.3% patients to progress earlier. CONCLUSIONS: Trial simulations supported selection of the approved Isa 10 mg/kg QW-Q2W regimen and showed that switching to a monthly regimen after 6 months may reduce clinical benefit in the overall population. However, patients with good prognostic characteristics and with a stable, very good partial response may switch to a monthly regimen after 6 months without compromising the risk of disease progression. This hypothesis will be tested in a prospective clinical trial.

Comparison of statistical methodologies used to estimate the treatment effect on time-to-event outcomes in observational studies.

The use of real-world data became more and more popular in the pharmaceutical industry. The impact of real-world evidence is now well emphasized by the regulatory authorities. Indeed, the analysis of this type of data can play a key role for treatment efficacy and safety. The aim of this work is to assess various methods and give guidance on the comparisons of drugs, mostly with respect to time-to-event data, in non-randomized studies with potentially confounding variables. For that purpose, several statistical methodologies are compared based on simulation studies. These methodologies belong to family classes of methods that are widely used for this type of problem: regression, matching, weighting and subclassification methods. The evaluation criteria used to compare methods performances are the relative bias, the mean square error, the coverage probability and the width of the confidence interval. In this paper, we consider different scenarios of dataset features in order to study the effect of the sample size, the number of covariates and the magnitude of the treatment effect on the statistical methodologies performances. These statistical analyses are conducted within a proportional hazard model framework. Furthermore, we highlight the advantage of using techniques to identify relevant covariates for time-to-event outcomes by comparing two variable selection methods under a frequentist and a Bayesian inference. Based on simulation results, recommendations on each of the family of methods are provided to guide decision making.

Matching-adjusted indirect comparisons: Application to time-to-event data.

The Matching-Adjusted Indirect Comparison method (MAIC) is a recent methodology that allows to perform indirect comparisons between two drugs assessed in two different studies, where individual patients data are available in only one of the two studies, the data of the other one being available in an aggregate format only. In this work, we have assessed the properties of the MAIC method and compared, through simulations, several ways of practical implementation of the method. We conclude that it is more efficient to match the treatment arms separately (match the two drugs to compare on one hand, and the control arms on the other hand) and use the Lasso technique to select the covariates for the matching step is better than matching a maximal set of covariates.

Design optimization for dose-finding trials: a review.

Dose selection is one of the most difficult and crucial decisions to make during drug development. As a consequence, the dose-finding trial is a major milestone in the drug development plan and should be properly designed. This article will review the most recent methodologies for optimizing the design of dose-finding studies: all of them are based on the modeling of the dose-response curve, which is now the gold standard approach for analyzing dose-finding studies instead of the traditional ANOVA/multiple testing approach. We will address the optimization of both fixed and adaptive designs and briefly outline new methodologies currently under investigation, based on utility functions.

Evaluation of non-linear-mixed-effect modeling to reduce the sample sizes of pediatric trials in type 2 diabetes mellitus.

Recruitment for pediatric trials in Type II Diabetes Mellitus (T2DM) is very challenging, necessitating the exploration of new approaches for reducing the sample sizes of pediatric trials. This work aimed at assessing if a longitudinal Non-Linear-Mixed-Effect (NLME) analysis of T2DM trial could be more powerful and thus require fewer patients than two standard statistical analyses commonly used as primary or sensitivity efficacy analysis: Last-Observation-Carried-Forward (LOCF) followed by (co)variance (AN(C)OVA) analysis at the evaluation time-point, and Mixed-effects Model Repeated Measures (MMRM) analysis. Standard T2DM efficacy studies were simulated, with glycated hemoglobin (HbA1c) as the main endpoint, 24 weeks' study duration, 2 arms, assuming a placebo and a treatment effect, exploring three different scenarios for the evolution of HbA1c, and accounting for a dropout phenomenon. 1000 trials were simulated, then analyzed using the 3 analyses, whose powers were compared. As expected, the longitudinal modeling MMRM analysis was found to be more powerful than the LOCF + ANOVA analysis at week 24. The NLME analysis gave slightly more accurate drug-effect estimations than the two other methods, however it tended to slightly overestimate the magnitude of the drug effect, and it was more powerful than the MMRM analysis only in some scenarios of slow HbA1c decrease. The gain in power afforded by NLME was more apparent when two additional assessments enriched the design; however, the gain was not systematic for all scenarios. Finally, this work showed that NLME analyses may help to reduce significantly the required sample sizes in T2DM pediatric studies, but only for enriched designs and slow HbA1c decrease.

Cardiac risk assessment based on early Phase I data and PK-QTc analysis is concordant with the outcome of thorough QTc trials: an assessment based on eleven drug candidates.

Cardiac safety assessment is a key regulatory requirement for almost all new drugs. Until recently, one evaluation aspect was via a specifically designated, expensive, and resource intensive thorough QTc study, and a by-time-point analysis using an intersection-union test (IUT). ICH E14 Q&A (R3) (http://www.ich.org/fileadmin/Public_Web_Site/ICH_Products/Guidelines/Efficacy/E14/E14_Q_As_R3__Step4.pdf) allows for analysis of the PK-QTc relationship using early Phase I data to assess QTc liability. In this paper, we compared the cardiac risk assessment based on the early Phase I analysis with that from a thorough QTc study across eleven drug candidate programs, and demonstrate that the conclusions are largely the same. The early Phase I analysis is based upon a linear mixed effect model with known covariance structure (Dosne et al. in Stat Med 36(24):3844-3857, 2017). The treatment effect was evaluated at the supratherapeutic Cmax as observed in the thorough QTc study using a non-parametric bootstrap analysis to generate 90% confidence intervals for the treatment effect, and implementation of the standardized methodology in R and SAS software yielded consistent results. The risk assessment based on the concentration-response analysis on the early Phase I data was concordant with that based on the standard analysis of the thorough QTc study for nine out of the eleven drug candidates. This retrospective analysis is consistent with and supportive of the conclusion of a previous prospective analysis by Darpo et al. (Clin Pharmacol Ther 97(4):326-335, 2015) to evaluate whether C-QTc analysis can detect QTc effects in a small study with healthy subjects.

Nonlinear joint models for individual dynamic prediction of risk of death using Hamiltonian Monte Carlo: application to metastatic prostate cancer.

BACKGROUND: Joint models of longitudinal and time-to-event data are increasingly used to perform individual dynamic prediction of a risk of event. However the difficulty to perform inference in nonlinear models and to calculate the distribution of individual parameters has long limited this approach to linear mixed-effect models for the longitudinal part. Here we use a Bayesian algorithm and a nonlinear joint model to calculate individual dynamic predictions. We apply this approach to predict the risk of death in metastatic castration-resistant prostate cancer (mCRPC) patients with frequent Prostate-Specific Antigen (PSA) measurements. METHODS: A joint model is built using a large population of 400 mCRPC patients where PSA kinetics is described by a biexponential function and the hazard function is a PSA-dependent function. Using Hamiltonian Monte Carlo algorithm implemented in Stan software and the estimated population parameters in this population as priors, the a posteriori distribution of the hazard function is computed for a new patient knowing his PSA measurements until a given landmark time. Time-dependent area under the ROC curve (AUC) and Brier score are derived to assess discrimination and calibration of the model predictions, first on 200 simulated patients and then on 196 real patients that are not included to build the model. RESULTS: Satisfying coverage probabilities of Monte Carlo prediction intervals are obtained for longitudinal and hazard functions. Individual dynamic predictions provide good predictive performances for landmark times larger than 12 months and horizon time of up to 18 months for both simulated and real data. CONCLUSIONS: As nonlinear joint models can characterize the kinetics of biomarkers and their link with a time-to-event, this approach could be useful to improve patient's follow-up and the early detection of most at risk patients.

Using the SAEM algorithm for mechanistic joint models characterizing the relationship between nonlinear PSA kinetics and survival in prostate cancer patients.

Joint modeling is increasingly popular for investigating the relationship between longitudinal and time-to-event data. However, numerical complexity often restricts this approach to linear models for the longitudinal part. Here, we use a novel development of the Stochastic-Approximation Expectation Maximization algorithm that allows joint models defined by nonlinear mixed-effect models. In the context of chemotherapy in metastatic prostate cancer, we show that a variety of patterns for the Prostate Specific Antigen (PSA) kinetics can be captured by using a mechanistic model defined by nonlinear ordinary differential equations. The use of a mechanistic model predicts that biological quantities that cannot be observed, such as treatment-sensitive and treatment-resistant cells, may have a larger impact than PSA value on survival. This suggests that mechanistic joint models could constitute a relevant approach to evaluate the efficacy of treatment and to improve the prediction of survival in patients.

Model averaging inconcentration-QT analyses.

This article describes how a frequentist model averaging approach can be used for concentration-QT analyses in the context of thorough QTc studies. Based on simulations, we have concluded that starting from three candidate model families (linear, exponential, and Emax) the model averaging approach leads to treatment effect estimates that are quite robust with respect to the control of the type I error in nearly all simulated scenarios; in particular, with the model averaging approach, the type I error appears less sensitive to model misspecification than the widely used linear model. We noticed also few differences in terms of performance between the model averaging approach and the more classical model selection approach, but we believe that, despite both can be recommended in practice, the model averaging approach can be more appealing because of some deficiencies of model selection approach pointed out in the literature. We think that a model averaging or model selection approach should be systematically considered for conducting concentration-QT analyses. Copyright © 2016 John Wiley & Sons, Ltd.

A drug and disease model for lixisenatide, a GLP-1 receptor agonist in type 2 diabetes.

Incretin hormone analogs such as glucagon-like peptide-1 (GLP-1) receptor agonists have emerged as promising new options for the treatment of type 2 diabetes mellitus (T2DM), targeting several of its pathophysiological traits, including reduced insulin sensitivity, inadequate insulin secretion, and loss of ß-cell mass (BCM). This article describes the semi-mechanistic modeling of lixisenatide dose-response over time using fasting plasma glucose (FPG), fasting serum insulin (FSI) and glycated hemoglobin (HbA1c) data from two Phase II and four Phase III clinical trials, for a total of 2470 T2DM patients. Previously published models for FPG, FSI, and BCM as well as HbA1c were adapted and expanded to describe the available data. The model incorporated aspects describing disease progression, standard-of-care, FPG-dependent and -independent HbA1c synthesis, and covariate effects of body size, race, and sex. The final model described lixisenatide effects on ß-cell responsiveness, insulin sensitivity and FPG-independent HbA1c synthesis, was able to describe the observed FPG, FSI, and HbA1c data accurately, and was successful in predicting data from an unseen Phase III clinical study.