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Many new opportunities surround rare pediatric disease drug development, thanks to key advances in regulatory thinking and in the scientific community. As rare disease drug development brings challenges to the developers in terms of limited understanding of natural history, heterogeneity in drug response, as well as difficulty recruiting patients in pivotal trials, there has never been a greater need for quantitative integration. To understand how International Consortium for Innovation and Quality in Pharmaceutical Development (IQ) member companies approach pediatric rare disease drug development, the rare pediatric subteam of the Clinical Pharmacology Leadership Group (CPLG) sponsored Pediatrics Working Group conducted a baseline survey to assess the four main pillars of this quantitative innovation, namely, biomarkers and surrogate end points, statistical methodologies, model‐informed drug development, as well as public–private partnerships. The survey was administered by IQ and yielded 13 evaluable responders from represented companies. This article presents the key findings from this baseline identifying survey, highlighting the key blind spots, and providing insightful expert opinions to address those gaps. In summary, we call an urgent attention to the community on the opportunities to enhance integration and within‐industry learnings from this analysis on aspects related to platform studies, end‐to‐end quantitative integration, and sharing of trial‐level placebo data for better understanding of disease progression and more efficient trial designs. We collectively hope that these findings will stimulate discussion and debate around cross‐industry sharing and collaboration to better delineate principles and further enhance the efficiency of rare pediatric disease drug development. [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]

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 β2‐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. [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]

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%5fWeb%5fSite/ICH%5fProducts/Guidelines/Efficacy/E14/E14%5fQ%5fAs%5fR3%5f%5fStep4.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. [ABSTRACT FROM AUTHOR]

A letter to the editor is presented in response to the article "Statistical Assessment of QT/QTc Prolongation Based on Maximum of Correlated Normal Random Variables," by Cheng and colleagues in the 2008 issue.