Omecamtiv mecarbil in chronic heart failure with reduced ejection fraction, GALACTIC‐HF: baseline characteristics and comparison with contemporary clinical trials

Aims: The safety and efficacy of the novel selective cardiac myosin activator, omecamtiv mecarbil, in patients with heart failure with reduced ejection fraction (HFrEF) is tested in the Global Approach to Lowering Adverse Cardiac outcomes Through Improving Contractility in Heart Failure (GALACTIC‐HF) trial. Here we describe the baseline characteristics of participants in GALACTIC‐HF and how these compare with other contemporary trials. Methods and Results: Adults with established HFrEF, New York Heart Association functional class (NYHA) ≥ II, EF ≤35%, elevated natriuretic peptides and either current hospitalization for HF or history of hospitalization/ emergency department visit for HF within a year were randomized to either placebo or omecamtiv mecarbil (pharmacokinetic‐guided dosing: 25, 37.5 or 50 mg bid). 8256 patients [male (79%), non‐white (22%), mean age 65 years] were enrolled with a mean EF 27%, ischemic etiology in 54%, NYHA II 53% and III/IV 47%, and median NT‐proBNP 1971 pg/mL. HF therapies at baseline were among the most effectively employed in contemporary HF trials. GALACTIC‐HF randomized patients representative of recent HF registries and trials with substantial numbers of patients also having characteristics understudied in previous trials including more from North America (n = 1386), enrolled as inpatients (n = 2084), systolic blood pressure < 100 mmHg (n = 1127), estimated glomerular filtration rate < 30 mL/min/1.73 m2 (n = 528), and treated with sacubitril‐valsartan at baseline (n = 1594). Conclusions: GALACTIC‐HF enrolled a well‐treated, high‐risk population from both inpatient and outpatient settings, which will provide a definitive evaluation of the efficacy and safety of this novel therapy, as well as informing its potential future implementation

Balancing the Objectives of Statistical Efficiency and Allocation Randomness in Randomized Controlled Trials

Various restricted randomization procedures are available to achieve equal (1:1) allocation in a randomized clinical trial. However, for some procedures, there is a nonnegligible probability of imbalance in the final numbers which may result in an underpowered study. It is important to assess such probability at the study planning stage and make adjustments in the design if needed. In this paper, we perform a quantitative assessment of the tradeoff between randomness, balance, and power of restricted randomization designs targeting equal allocation. First, we study the small-sample performance of biased coin designs with known asymptotic properties and identify a design with an excellent balance–randomness tradeoff. Second, we investigate the issue of randomization-induced treatment imbalance and the corresponding risk of an underpowered study. We propose two risk mitigation strategies: increasing the total sample size or fine-tuning the biased coin parameter to obtain the least restrictive randomization procedure that attains the target power with a high, user-defined probability for the given sample size. Our approach is simple and yet generalizable to more complex settings, including trials with stratified randomization and multi-arm trials with possibly unequal randomization ratios

Clinical design and analysis strategies for development of cell and gene therapies: quantitative drug development in the age of genetic medicine

Cell and gene therapies have shown enormous promise across a range of diseases in recent years. Numerous adoptive cell therapy modalities as well as systemic and direct to target tissue gene transfer administrations are currently in clinical development. The clinical trial design, development, analysis, and reporting of novel cell and gene therapies can differ significantly from established practices for small molecule drugs and biologics. Here we discuss important quantitative considerations and key competencies for drug developers in the preclinical, trial design, and lifecycle planning for gene therapies. We argue that the unique development path of gene therapies requires practicing quantitative drug developers—statisticians, pharmacometricians, pharamcokineticists, and medical and operational leads—to exercise active collaboration and cross-functional learning across development stages

Investigating the value of glucodensity analysis of continuous glucose monitoring data in type 1 diabetes: An exploratory analysis

Introduction: Continuous glucose monitoring (CGM) devices capture longitudinal data on interstitial glucose levels and are increasingly used to show the dynamics of diabetes metabolism. Given the complexity of CGM data, it is crucial to extract important patterns hidden in these data through efficient visualization and statistical analysis techniques. Methods: In this paper, we adopted the concept of glucodensity, and using a subset of data from an ongoing clinical trial in pediatric individuals and young adults with new-onset type 1 diabetes, we performed a cluster analysis of glucodensities. We assessed the differences among the identified clusters using analysis of variance (ANOVA) with respect to residual pancreatic beta cell function and some standard CGM-derived parameters such as time in range, time above range, and time below range. Results: Distinct CGM data patterns were identified using cluster analysis based on glucodensities. Statistically significant differences were shown among the clusters with respect to baseline levels of pancreatic beta-cell function surrogate (C-peptide) and with respect to time in range and time above range. Discussion: Our findings provide supportive evidence for the value of glucodensity in the analysis of CGM data. Some challenges in the modeling of CGM data include unbalanced data structure, missing observations, and many known and unknown confounders, which speaks to the importance of–and provides opportunities for–taking an approach integrating clinical, statistical, and data science expertise in the analysis of these data

Which Randomization Methods Are Used Most Frequently in Clinical Trials? Results of a Survey by the Randomization Working Group

In June–July 2022, the Randomization Working Group (https://randomization-working-group.rwthaachen.de) conducted an online survey on the current practices in the application of randomization in clinical trials. Of 145 unique respondents, 137 (94.5%) identified themselves as statisticians. The majority of respondents were from academia and pharmaceutical companies. Permuted block randomization, with or without stratification, was the most frequently chosen method of randomization for RCTs. Interactive Web-Based Response Systems (IWRS) and “in-house” or a combination of “in-house and outsourced” randomization models were found to be most common in practice. Over 80% of respondents perceived some challenges to adoption of new randomization methods that may have more desirable properties. Over 80% of respondents identified opportunities for improving current practice, including education/training, development of standards/guidance on randomization, and adoption of validated software for generating randomization sequences. In summary, practitioners acknowledge the pivotal role of randomization in clinical trials. There are some perceived challenges to successful implementation of randomization, and there are opportunities for improving practice

Suppression subtractive hybridization: a method for generating differentially regulated or tissue-specific cDNA probes and libraries

A new and highly effective method, termed suppression subtractive hybridization (SSH), has been developed for the generation of subtracted cDNA libraries. It is based primarily on a recently described technique called suppression PCR and combines normalization and subtraction in a single procedure. The normalization step equalizes the abundance of cDNAs within the target population and the subtraction step excludes the common sequences between the target and driver populations. In a model system, the SSH technique enriched for rare sequences over 1,000-fold in one round of subtractive hybridization. We demonstrate its usefulness by generating a testis-specific cDNA library and by using the subtracted cDNA mixture as a hybridization probe to identify homologous sequences in a human Y chromosome cosmid library. The human DNA inserts in the isolated cosmids were further confirmed to be expressed in a testis-specific manner. These results suggest that the SSH technique is applicable to many molecular genetic and positional cloning studies for the identification of disease, developmental, tissue-specific, or other differentially expressed genes

A study of new exploratory tools, digital technologies and biomarkers to characterize depression

Background: Digital technologies have the potential to provide objective and precise tools to detect depression-related symptoms. Deployment of digital technologies in clinical research can enable collection of large volumes of clinically relevant data that may not be captured using conventional psychometric questionnaires and patient-reported outcomes. Rigorous methodology studies to develop novel digital endpoints in depression are warranted. Objective: We conducted an exploratory, cross-sectional study to assess feasibility of several novel digital technologies in subjects with major depressive disorder (MDD) and normal healthy controls. The study aimed at assessing utility and accuracy of the digital technologies as potential diagnostic tools for MDD, as well as correlating digital biomarkers to clinically validated psychometric questionnaires in depression. Methods: A cross-sectional, non-interventional study of 20 subjects with MDD and 20 normal healthy volunteers was conducted at the Centre for Human Drug Research (CHDR), the Netherlands. Eligible participants attended three in-clinic visits (days 1, 7, and 14), at which they underwent a series of assessments, including conventional clinical psychometric questionnaires and novel digital technologies. Between the visits, there was at-home collection of data through mobile applications. In all, eight digital technologies were evaluated in this study. Results: Our data analysis was organized by technology – to better understand individual features of various technologies. In many cases, we obtained simple, parsimonious models that have reasonably high diagnostic accuracy and potential to predict standard clinical outcome in depression. Conclusion: This study generated many useful insights for future methodology studies of digital technologies and proof of-concept clinical trials in depression and possibly other indications