Clinical effectiveness of guided breathing exercises in reducing anxiety, stress, and depression in COVID-19 patients

Adaptive Platform Trials: Definition, Design, Conduct And Reporting Considerations

By: Derek C. Angus, Brian M. Alexander, Scott Berry, Meredith Buxton, Roger Lewis, Melissa Paoloni, Steven A. R. Webb, Steven Arnold, Anna Barker, Donald A. Berry, Marc J. M. Bonten, Mary Brophy, Christopher Butler, Timothy F. Cloughesy, Lennie P. G. Derde, Laura J. Esserman, Ryan Ferguson, Louis Fiore, Sarah C. Gaffey, J. Michael Gaziano, Kathy Giusti, Herman Goossens, Stephane Heritier, Bradley Hyman, Michael Krams, Kay Larholt, Lisa M. LaVange, Philip Lavori, Andrew W. Lo, Alexander J. London, Victoria Manax, Colin McArthur, Genevieve O'Neill, Giovanni Parmigiani, Jane Perlmutter, Elizabeth A. Petzold, Craig Ritchie, Kathryn M. Rowan, Christopher W. Seymour, Nathan I. Shapiro, Diane M. Simeone, Bradley Smith, Bradley Spellberg, Ariel Dora Stern, Lorenzo Trippa, Mark Trusheim, Kert Viele, Patrick Y. Wen and Janet Woodcock

Abstract

Researchers, clinicians, policymakers, and patients are increasingly interested in questions about therapeutic interventions that are difficult or costly to answer with traditional, free-standing, parallel-group randomized controlled trials (RCTs). Examples include scenarios in which there is a desire to compare multiple interventions, to generate separate effect estimates across subgroups of patients with distinct but related conditions or clinical features, or to minimize downtime between trials. In response, researchers have proposed new RCT designs such as adaptive platform trials (APTs), which are able to study multiple interventions in a disease or condition in a perpetual manner, with interventions entering and leaving the platform on the basis of a predefined decision algorithm. APTs offer innovations that could reshape clinical trials, and several APTs are now funded in various disease areas. With the aim of facilitating the use of APTs, here we review common features and issues that arise with such trials and offer recommendations to promote best practices in their design, conduct, oversight, and reporting.

Keywords Citation Angus, Derek C., Brian M. Alexander, Scott Berry, Meredith Buxton, Roger Lewis, Melissa Paoloni, Steven A. R. Webb, Steven Arnold, Anna Barker, Donald A. Berry, Marc J. M. Bonten, Mary Brophy, Christopher Butler, Timothy F. Cloughesy, Lennie P. G. Derde, Laura J. Esserman, Ryan Ferguson, Louis Fiore, Sarah C. Gaffey, J. Michael Gaziano, Kathy Giusti, Herman Goossens, Stephane Heritier, Bradley Hyman, Michael Krams, Kay Larholt, Lisa M. LaVange, Philip Lavori, Andrew W. Lo, Alexander J. London, Victoria Manax, Colin McArthur, Genevieve O'Neill, Giovanni Parmigiani, Jane Perlmutter, Elizabeth A. Petzold, Craig Ritchie, Kathryn M. Rowan, Christopher W. Seymour, Nathan I. Shapiro, Diane M. Simeone, Bradley Smith, Bradley Spellberg, Ariel Dora Stern, Lorenzo Trippa, Mark Trusheim, Kert Viele, Patrick Y. Wen, and Janet Woodcock. "Adaptive Platform Trials: Definition, Design, Conduct and Reporting Considerations." Nature Reviews: Drug Discovery 18, no. 10 (October 2019): 797–807.

HEALEY ALS Trial To Allow Longer Follow-up, Blood Cell Collection

The HEALEY ALS platform trial, which is simultaneously testing multiple potential treatments for amyotrophic lateral sclerosis (ALS), is amending its master protocol to allow a longer follow-up time and collection of blood cells for use in future research.

Slight modifications to the enrollment criteria and visit schedule will also be made, all of which were presented earlier this month at the 35th International Symposium on ALS/MND in Montreal, Canada.

"These updates to the master protocol will significantly strengthen the trial's ability to identify effective treatments for ALS and bring us closer to finding answers for this devastating disease," Merit Cudkowicz, MD, principal investigator and sponsor of the HEALEY ALS trial, said in a press release. The study is being led by Massachusetts General Hospital's Sean M. Healey & AMG Center for ALS, of which Cudkowicz is director.

The HEALEY ALS trial (NCT04297683) is testing different experimental ALS treatments at the same time. In each arm, 160 participants are randomly assigned to receive either the experimental therapy (120 patients) or a placebo (40 patients). Because each treatment arm uses the same eligibility criteria, data from patients given each treatment can be compared with data from those given a placebo across all arms of the study. This design allows new treatments to be tested faster and more efficiently.

HEALEY has provided meaningful outcomes for 5 experimental therapies

Since its inception in 2020, the study has provided meaningful outcomes for five different investigational therapies. Two of them, CNM-Au8 and pridopridine, showed promise in HEALEY ALS and are now headed toward Phase 3 clinical testing. The other three (zilucoplan, verdiperstat, and SLS-005) failed to show efficacy and are no longer in development.

Additional arms testing two more treatments, ABBV-CLS-7262 and DNL343, have finished enrolling patients and are still ongoing. More arms are expected to launch in the new year.

"We learned a lot from the first regimens in the HEALEY ALS Platform Trial," Cudkowicz said, noting the new amendments were designed based on lessons learned from the first regimens.

Previously, each arm of the HEALEY ALS trial included a follow-up time of 24 weeks (about six months). With the new amendments, the follow-up time will now be 36 weeks (about nine months). The longer follow-up time is expected to allow researchers to better assess the efficacy of therapies that may take a while to show an effect, providing a clearer picture of the potential long-term benefits of treatment.

The amendments also changed the inclusion criteria. While the study had previously been open to patients who were up to three years from the onset of ALS symptoms, it is now including only patients who are no more than two years out from symptom onset. There also have been changes to the clinical visit schedule, along with more opportunities for remote visits, to allow patients more flexibility and convenience.

Another change is that the study will be collecting blood cells (specifically peripheral blood mononuclear cells or PBMCs), which will be stored for future ALS research. Of note, PBMCs can be used to generate induced pluripotent stem cells, which are lab-engineered stem cells that can then be grown into nerve cells for studies that seek to understand how nerves are affected by ALS using cells from patients.

How The Right Clinical Trial Design Software Boosts Success Rates

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The clinical development process is long, often exceeding 10 years from discovery through regulatory approval for a drug candidate. Whereas established companies have experience in all clinical trial phases, in-house regulatory expertise, and are equipped to run large, standardized trials, smaller biotechs have limited financing, fewer established processes, and may outsource parts of their drug development process to specialized vendors. In this article, we'll explore challenges in clinical trial design and show how choosing the right software solution can drastically improve the probability of clinical trial success.

Challenges in clinical trial design

Progressing a drug through clinical trials relies on a robust evidence generation plan that includes a strong trial design, accurate patient recruitment and treatment effect assumptions, and regulatory filing expertise. Considerations for the patient population, research question, data analysis, and addressing statistical significance can be challenging for many reasons.

(1) Patient population: Recruiting patients for clinical trials has always been a challenge, including the need to consider the number of patients, patient experience, and population diversity to ensure the study population is appropriate and sufficient to address research questions. For rare diseases, recruiting a sufficient number of eligible patients poses another difficulty.

(2) Research question: A clinical trial should be appropriately designed to address the research question. One method used to create a clinical trial research question is the PICO framework, which provides a standard format for defining the assessment scope as population (P), intervention (I), comparator (C), and outcomes (O). This framework helps define clear and focused research questions, ensuring the clinical trial is designed to answer them effectively.

(3) Data handling and analysis: Clinical trials generate vast amounts of data, and it is therefore important for sponsors to consider how the data will be collected, stored, and analyzed. This can include designing the database, planning statistical analysis, quality control, and data cleaning. These steps can facilitate the generation of accurate results for a regulatory submission.

(4) Statistical significance: Having the right number of trial participants is necessary to ensure that the results are statistically significant. However, recruiting more patients than needed for statistical power may constrain sponsors' resources.

Why is it important to have a reliable clinical trial design software?

When it comes to designing a clinical trial, the right software can be instrumental to design analysis and selection, and ultimately has implications for cost, duration, and probability of success for the sponsors. Clinical trial design software can help determine the best-fit trial designs that can stop early if they seem likely to fail or continue if they are likely to be successful.

Clinical trial design software can help de-risk trial execution through simulation-guided design. Some unknown assumptions in trial design include patient accrual rates, patient dropout rates, endpoint selection, and the true underlying treatment effect. "Once you actually get a basic clinical trial design, you want to evaluate that design across a wide variety of assumptions," said Kyle Wathen, VP of Scientific Strategy and Innovation at Cytel, a leader in software and services for evidence generation for life science companies. For example, simulating patient recruitment parameters along with other variables in the design, can help determine how many total participants to recruit for the study to ensure that the results have sufficient statistical power.

Assessing sufficient possible design scenarios requires robust computing power. "These are not trivial calculations. These are operations that require huge amounts of computing power," said Kevin Trimm, Chief Product Officer at Cytel. "It's important for the software to compute quickly and efficiently, because otherwise, you'd be waiting for prolonged periods of time, making the process of design more cumbersome," he added. Cytel's clinical trial design software was created with this challenge in mind. "We have a very large computing grid that's associated with the platform," said Trimm.

This contrasts with the first iterations of clinical trial design software that were created in the 1980s. "What they did was all based on analytical computation, so there were certain assumptions that had to be made in the design process, and statisticians were very limited in what they could do," said Wathen. These software solutions were also limited to the computational power of the device on which they were installed. "It's grown a lot from standard-type fixed design, where you get the sample size you need for the power you're looking for, and you run with those basic assumptions, versus adaptive designs where you may look at interim data and change how the trial is running based on that data," Wathen said.

Another important feature that Cytel has implemented in its clinical trial design software is the ability to integrate custom R coding into the software directly, which allows users to stay current with new analysis techniques. For example, "if a new analysis method comes out next week in a statistical journal, it could be implemented and loaded onto our platform more rapidly using R code, which would allow statisticians to compare to other, more standard analysis types," said Wathen. This gives our users a lot of flexibility to be able to compare several design options side-by-side "as opposed to having to piece together results in different software from other vendors," he added.

Optimizing adaptive clinical trial design: the need for flexible, efficient software

In 1987, Cytel launched its first biostatistics software package, Xact, consisting of the StatXact®, and LogXact® software solutions, followed by its flagship adaptive clinical trial design software, EAST® (acronym for Early Stopping), in 1995.

"The term early stopping tends to have a negative connotation, because people hope that the trial is going to show efficacy. But the truth is that 90% of trials don't, and you want to stop them early if they are not showing early signs of success. That's the premise behind adaptive and group sequential designs."

Kevin Trimm, Chief Product Officer at Cytel

Adaptive clinical trials are designed to allow for pre-determined changes during the trial based on interim results. "The idea is to make it much more likely to detect treatments that are beneficial to patients and weed out those that are unsafe or ineffective quicker as well, so that you're not wasting resources or jeopardizing patients unnecessarily," said Wathen.

Now, two decades after Cytel launched its EAST software, their new East Horizon™ platform is solving current challenges in adaptive clinical trial design by increasing user flexibility in using open-source software (R coding), increasing available computing power, and providing a user-friendly interface and visualizations. "Cytel has created many software solutions related to clinical trial design over the years," said Trimm. "One of the things that we're accomplishing with the East Horizon platform is to bring all of that innovation into a single platform so that everything that you would want to do concerning statistical clinical trial design can be accomplished in one place."

This new software has helped Bristol Myers Squibb (BMS) optimize their Phase III clinical trial for a hematology disease drug candidate. In partnership with Cytel, BMS used the Explore tool within the East Horizon platform to understand if they could shorten study duration using an early readout without sacrificing study power. Using the software, BMS statisticians simulated over 5,000 designs across 18 potential treatment effects and enrollment combinations. The recommended design included two interim analyses, shortened trial duration by 13 months, and required fewer patients when compared to the original study design.

"There's savings, obviously, to the company," said Trimm. "But there is also the benefit to patients in the real world waiting for medicines to be approved. Now the medicine is available to them, potentially 13 months earlier, because of a smarter design. It could be lifesaving, if not just life improving."

Clinical trial design software has improved dramatically over the decades. By enhancing computing power and flexibility in new methodology, software like the East Horizon platform can help advance therapeutics toward approval more efficiently with benefits to drug developers and patients alike.

Design a better clinical trial with Cytel.

East Horizon™, East®, StatXact®, and LogXact® are trademarks or registered trademarks of Cytel Inc. All rights reserved.

Image Courtesy: Cytel

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