Adaptive Trial Design Research Articles

CTNI-39. UPDATE ON GBM AGILE: A GLOBAL, PHASE 2/3 ADAPTIVE PLATFORM TRIAL TO EVALUATE MULTIPLE REGIMENS IN NEWLY DIAGNOSED AND RECURRENT GLIOBLASTOMA

Abstract BACKGROUND GBM AGILE (Glioblastoma Adaptive, Global, Innovative Learning Environment) is a biomarker based, multi-arm, international, seamless Phase 2/3 platform trial designed to rapidly identify and confirm experimental therapies that improve overall survival for approval along with their associated biomarker signatures. GBM AGILE is a collaboration between academic investigators, patient organizations, and industry to support new drug applications for newly diagnosed (ND) and recurrent glioblastoma. METHODS The primary objective of GBM AGILE is to identify therapies that improve the overall survival in patients with ND or recurrent glioblastoma. Bayesian response adaptive randomization is used within subtypes of the disease to assign participants to investigational arms based on their performance. GBM AGILE operates under a Master Protocol allowing multiple drugs to be evaluated simultaneously and over time against a common control. New experimental therapies are added as information about promising new drugs is identified, while therapies are removed as they complete evaluation. Six investigational arms have been included to date. Along with an adaptive trial design, shared control arm and operational processes, GBM AGILE continues to incorporate new design and operational elements. One recent design element added to the Master Protocol for consideration for future arms is an extended evaluation period, of up to 11 months, for arms that reach maximum sample size in Stage 1 (Stage 1: learn most responsive signature) without having reached a decision for futility or graduation. Allowing data to mature and additional events to accumulate may increase the ability of the trial to detect efficacy, and if appropriate, for an arm to graduate to Stage 2 (Stage 2: confirm activity in graduating signature). Using improved and flexible processes, GBM AGILE continues to serve as a global trial that supports the efficient and rapid incorporation and evaluation of new experimental therapies for patients with glioblastoma. NCT number: NCT03970447.

The Great Controversy of Obstructive Sleep Apnea Treatment for Cardiovascular Risk Benefit: Advancing the Science Through Expert Consensus. An Official American Thoracic Society Workshop Report.

The prevalence of obstructive sleep apnea (OSA) is on the rise, driven by various factors including more sensitive diagnostic criteria, increased awareness, enhanced technology through at-home testing enabling easy and cost-effective diagnosis, and a growing incidence of comorbid conditions such as obesity. Treating symptomatic patients with OSA syndrome to enhance quality of life remains a cornerstone approach. However, there is a lack of consensus regarding treatment to improve cardiovascular disease (CVD) outcomes, particularly in light of overall negative results from several randomized controlled trials (RCT) indicating no benefit of positive airway pressure (PAP) therapy on primary and secondary CVD events. These RCTs were limited by suboptimal PAP adherence, use of composite CVD outcomes, and limited diversity and generalizability to Sleep Clinic patients. As such, this workshop assembled clinical experts, as well as researchers in basic and translational science, epidemiology, clinical trials, and population health to discuss the current state, and future research directions to guide personalized therapeutic strategies and future research directions in OSA. There was overall consensus among workshop participants that OSA represents a heterogeneous disease with variable endotypes and phenotypes, and heterogeneous responses to treatment. Future research should prioritize employing multi-modal therapeutic approaches within innovative and adaptive trial designs, focusing on specific subgroups of OSA patients hypothesized to benefit from a CVD perspective. Future work should also be inclusive of diverse populations and consider the life-course of OSA to better comprehend treatment strategies that can address the disproportionate impact of OSA on racially minoritized groups. Further, a more holistic approach to sleep must be adopted to include broader assessments of symptoms, sleep duration, and comorbid sleep and circadian disorders. Finally, it is imperative to establish a sleep research consortium dedicated to collecting raw data and biospecimens categorized by OSA subtypes. This will facilitate mechanistic determinations, foster collaborative research, and help bolster the pipeline of early-career researchers.

Ensuring safety and monitoring adverse events in clinical trials: Challenges and innovations

Safety and adverse events (AEs) monitoring in clinical trials are critical to ensuring participant safety and the successful development of new therapies. Effective monitoring not only protects patients but also helps identify any risks associated with the investigational product. This paper elaborates on the methodologies used to detect, categorize, and report adverse events during clinical trials, and how these processes are governed by stringent regulatory frameworks. Additionally, the discussion highlights the role of pharmacovigilance systems, the ethical obligations researchers face in AE reporting, and the technological innovations that are transforming safety monitoring. Challenges, including under-reporting, data variability, and interpretation difficulties, are examined in depth. Ultimately, advancements in real-time data collection, adaptive trial designs, and artificial intelligence (AI) are promising to improve the accuracy and timeliness of adverse event monitoring, shaping the future of clinical research and patient safety.

Innovative approaches to risk management in clinical research: Balancing ethical standards, regulatory compliance, and intellectual property concerns

This study explores innovative approaches to risk management in clinical research, focusing on the intricate balance between ethical standards, regulatory compliance, and intellectual property (IP) concerns. As clinical trials become increasingly complex and globalized, managing risks effectively while maintaining participant safety and upholding legal and ethical principles has become paramount. The study adopts a comprehensive review of emerging technologies, such as artificial intelligence (AI), machine learning (ML), blockchain, and big data analytics, to assess their role in transforming risk management strategies. Key findings reveal that AI and predictive analytics significantly enhance real-time monitoring and the early identification of risks, while adaptive trial designs offer flexibility in managing unforeseen challenges during trials. Blockchain technology improves transparency and data integrity, ensuring secure and verifiable data transactions. However, the intersection of IP protections and equitable access to treatments presents ongoing ethical challenges, particularly in low-income regions. The study concludes that while technological innovations provide robust tools for mitigating risks, a balanced approach to regulatory frameworks and IP protections is essential. The study recommends increased collaboration between regulatory bodies, research institutions, and technology providers to harmonize global regulatory standards, ensuring both innovation and accessibility. Furthermore, it advocates for more flexible IP frameworks that address the tension between protecting innovations and ensuring equitable access to life-saving treatments.

Adaptive Trials in Stroke: Current Use and Future Directions.

Inclusion of adaptive design features in a clinical trial provides preplanned flexibility to dynamically modify a trial during its conduct while preserving validity and integrity. Adaptive trials are needed to accelerate the conduct of more efficient, informative, and ethical clinical research in the field of neurology. Stroke is a natural candidate for adoption of these innovative approaches to trial design. This Research Methods in Neurology article is informed by a scoping review that identified 45 completed or ongoing adaptive clinical trials in stroke that were appraised: 15 trials had published results with or without a published protocol and 30 ongoing trials (14 trials had a published protocol, and 16 trials were registered only). Interventions spanned acute (n = 28), rehabilitation (n = 8), prevention (n = 8), and rehabilitation and prevention (n = 1). A subsample of these trials was selected to illustrate the utility of adaptive design features and discuss why each adaptive feature was incorporated in the design to best achieve the aim; whether each individual feature was used and whether it resulted in expected efficiencies; and any learnings during preparation, conduct, or reporting. We then discuss the operational, ethical, and regulatory considerations that warrant careful consideration during adaptive trial planning and reflect on the workforce readiness to deliver adaptive trials in practice. We conclude that adaptive trials can be designed, funded, conducted, and published for a wide range of research questions and offer future directions to support adoption of adaptive trial designs in stroke and neurologic research more broadly.

Adaptive enrichment trial designs using joint modelling of longitudinal and time-to-event data.

Adaptive enrichment allows for pre-defined patient subgroups of interest to be investigated throughout the course of a clinical trial. These designs have gained attention in recent years because of their potential to shorten the trial's duration and identify effective therapies tailored to specific patient groups. We describe enrichment trials which consider long-term time-to-event outcomes but also incorporate additional short-term information from routinely collected longitudinal biomarkers. These methods are suitable for use in the setting where the trajectory of the biomarker may differ between subgroups and it is believed that the long-term endpoint is influenced by treatment, subgroup and biomarker. Methods are most promising when the majority of patients have biomarker measurements for at least two time points. We implement joint modelling of longitudinal and time-to-event data to define subgroup selection and stopping criteria and we show that the familywise error rate is protected in the strong sense. To assess the results, we perform a simulation study and find that, compared to the study where longitudinal biomarker observations are ignored, incorporating biomarker information leads to increases in power and the (sub)population which truly benefits from the experimental treatment being enriched with higher probability at the interim analysis. The investigations are motivated by a trial for the treatment of metastatic breast cancer and the parameter values for the simulation study are informed using real-world data where repeated circulating tumour DNA measurements and HER2 statuses are available for each patient and are used as our longitudinal data and subgroup identifiers, respectively.

Enhancing DHA supplementation adherence: A Bayesian approach with finite mixture models and irregular interim schedules in adaptive trial designs.

Docosahexaenoic acid (DHA) supplementation has proven beneficial in reducing preterm births. However, the challenge lies in addressing nonadherence to prescribed supplementation regimens-a hurdle that significantly impacts clinical trial outcomes. Conventional methods of adherence estimation, such as pill counts and questionnaires, usually fall short when estimating adherence within a specific dosage group. Thus, we propose a Bayesian finite mixture model to estimate adherence among women with low baseline red blood cell phospholipid DHA levels (<6%) receiving higher DHA doses. In our model, adherence is defined as the proportion of participants classified into one of the two distinct components in a normal mixture distribution. Subsequently, based on the estimands from the adherence model, we introduce a novel Bayesian adaptive trial design. Unlike conventional adaptive trials that employ regularly spaced interim schedules, the novelty of our proposed trial design lies in its adaptability to adherence percentages across the treatment arm through irregular interims. The irregular interims in the proposed trial are based on the effect size estimation informed by the finite mixture model. In summary, this study presents innovative methods for leveraging the capabilities of Bayesian finite mixture models in adherence analysis and the design of adaptive clinical trials.

Tricuspid transcatheter edge-to-edge repair for severe tricuspid regurgitation: 1-year outcomes from the TRILUMINATE randomized cohort.

Tricuspid regurgitation (TR) is a right-sided valvular disease independently associated with morbidity and mortality. The TRILUMINATE Pivotal trial is the first randomized, controlled trial assessing the impact of TR reduction with tricuspid transcatheter edge-to-edge repair (T-TEER). Outcomes from the full randomized cohort of the TRILUMINATE Pivotal trial have not been previously reported, and the additional enrollment may further support the safety and effectiveness of T-TEER through 1 year. The TRILUMINATE Pivotal trial is an international RCT of T-TEER with the TriClip device in patients with symptomatic, severe TR. Adaptive trial design allowed enrollment past the primary analysis population. The primary outcome was a hierarchical composite of all-cause mortality or tricuspid valve surgery, heart failure hospitalizations (HFH), and quality-of-life improvement measured by Kansas City Cardiomyopathy Questionnaire (KCCQ) at 1 year. Between August 21, 2019 and June 29, 2022, 572 subjects were randomized, including the primary cohort (n=350) and subsequent enrollment (n=222). Subjects were elderly (78.1±7.8 years) and predominantly female (58.9%), with atrial fibrillation (87.8%) and prior HFH (23.8%). The primary endpoint was met for the full cohort (win ratio=1.84, p<0.0001). Freedom from all-cause mortality and tricuspid valve surgery through 12 months was 90.6% and 89.9% for the device and control groups, respectively (p=0.82). Annualized HFH rate was comparable between device and control subjects (0.17 vs 0.20 events/patient-year, p=0.40). A significant treatment effect was observed for change in quality of life with 49.5% of device subjects achieving a ≥15-point KCCQ score improvement (compared to 25.6% of control subjects, p<0.0001). All secondary endpoints favored T-TEER: moderate or less TR at 30 days (88.9% vs 5.3%, p<0.0001), KCCQ change at 1 year (13.0±1.4 vs -0.5±1.4 points, p<0.0001), and six-minute walk distance change at 1 year (1.7±7.5 vs -27.4±7.4 meters, p<0.0001). Freedom from major adverse events was 98.9% for T-TEER (vs. performance goal: 90%, p<0.0001). TriClip was safe and effective in the full randomized cohort of TRILUMINATE Pivotal with significant TR reduction and improvements in six-minute walk distance and health status. Rates of all-cause mortality or TV surgery and HFH through 1 year were not reduced by T-TEER.

Datopotamab-deruxtecan plus durvalumab in early-stage breast cancer: the sequential multiple assignment randomized I-SPY2.2 phase 2 trial.

Sequential adaptive trial designs can help accomplish the goals of personalized medicine, optimizing outcomes and avoiding unnecessary toxicity. Here we describe the results of incorporating a promising antibody-drug conjugate, datopotamab-deruxtecan (Dato-DXd) in combination with programmed cell death-ligand 1 inhibitor, durvalumab, as the first sequence of therapy in the I-SPY2.2 phase 2 neoadjuvant sequential multiple assignment randomization trial for high-risk stage 2/3 breast cancer. The trial includes three blocks of treatment, with initial randomization to different experimental agent(s) (block A), followed by a taxane-based regimen tailored to tumor subtype (block B), followed by doxorubicin-cyclophosphamide (block C). Subtype-specific algorithms based on magnetic resonance imaging volume change and core biopsy guide treatment redirection after each block, including the option of early surgical resection in patients predicted to have a high likelihood of pathologic complete response, which is the primary endpoint assessed when resection occurs. There are two primary efficacy analyses: after block A and across all blocks for six prespecified HER2-negative subtypes (defined by hormone receptor status and/or response-predictive subtypes). In total, 106 patients were treated with Dato-DXd/durvalumab in block A. In the immune-positive subtype, Dato-DXd/durvalumab exceeded the prespecified threshold for success (graduated) after block A; and across all blocks, pathologic complete response rates were equivalent to the rate expected for the standard of care (79%), but 54% achieved that result after Dato-DXd/durvalumab alone (block A) and 92% without doxorubicin-cyclophosphamide (after blocks A + B). The treatment strategy across all blocks graduated in the hormone-negative/immune-negative subtype. No new toxicities were observed. Stomatitis was the most common side effect in block A. No patients receiving block A treatment alone had adrenal insufficiency. Dato-DXd/durvalumab is a promising therapy combination that can eliminate standard chemotherapy in many patients, particularly the immune-positive subtype.ClinicalTrials.gov registration: NCT01042379 .

Transformative Role of Artificial Intelligence in the Pharmaceutical Sector

Background: The pharmaceutical sector is a critical component of healthcare, driving innovation in drug discovery, development, and delivery. With the increasing integration of artificial intelligence (AI), digital health technologies, and biotechnology, the industry is transforming rapidly. This review examines the key areas of the pharmaceutical industry and highlights the growing impact of AI in enhancing various processes, from drug discovery to clinical trials. To explore the applications of AI in drug discovery, development, manufacturing, clinical trials, personalized medicine, and regulatory compliance. This review also addresses the challenges, such as data privacy and interoperability, that accompany the adoption of AI in the pharmaceutical sector. Methods: A comprehensive review of existing literature and case studies on the application of AI in pharmaceutical research and operations was conducted. Key areas of focus include AI's role in predictive analytics, target identification, manufacturing, supply chain management, clinical trial optimization, and pharmacovigilance. Results: AI significantly enhances drug discovery by improving target identification, predictive modeling, and high-throughput screening. It optimizes manufacturing through real-time quality control and process automation. In clinical trials, AI facilitates patient recruitment and adaptive trial designs, while in personalized medicine, it enables biomarker discovery and treatment optimization. AI also supports regulatory compliance through automated monitoring and risk assessment. Conclusion: AI is transforming the pharmaceutical sector, making processes more efficient, precise, and tailored to individual patients. However, challenges such as data privacy, ethical considerations, and interoperability must be addressed to fully harness AI's potential. Standardization and collaboration will be essential in driving the next phase of innovation in pharmaceutical development and healthcare delivery.

Improving Miscarriage Prevention Research: a survey exploring the expectations of service users and stakeholders (IMPRESS) – a study protocol for a UK-based survey

IntroductionInterventional clinical trials in recurrent miscarriage use varying expected effect sizes to inform their sample size calculations. Often these are not informed by what stakeholders consider a meaningful treatment effect....

DHA supplementation for early preterm birth prevention: An application of Bayesian finite mixture models to adaptive clinical trial design optimization

BackgroundEarly preterm birth (ePTB) - born before 34 weeks of gestation - poses a significant public health challenge. Two randomized trials indicated an ePTB reduction among pregnant women receiving high-dose docosahexaenoic acid (DHA) supplementation. One of them is Assessment of DHA on Reducing Early Preterm Birth (ADORE). A survey employed in its secondary analysis identified women with low DHA levels, revealing that they derived greater benefits from high-dose DHA supplementation. This survey's inclusion in future trials can provide critical insights for informing clinical practices. ObjectiveTo optimize a Phase III trial design, ADORE Precision, aiming at assessing DHA supplement (200 vs. 1000 mg/day) on reducing ePTB among pregnant women with a low baseline DHA. MethodsWe propose a Bayesian Hybrid Response Adaptive Randomization (RAR) Design utilizing a finite mixture model to characterize gestational age at birth. Subsequently, a dichotomized ePTB outcome is used to inform trial design using RAR. Simulation studies were conducted to compare a Fixed Design, an Adaptive Design with early stopping, an ADORE-like Adaptive RAR Design, and two new Hybrid Designs with different hyperpriors. DiscussionSimulation reveals several advantages of the RAR designs, such as higher allocation to the more promising dose and a trial duration reduction. The proposed Hybrid RAR Designs addresses the statistical power drop observed in Adaptive RAR. The new design model shows robustness to hyperprior choices. We recommend Hybrid RAR Design 1 for ADORE Precision, anticipating that it will yield precise determinations, which is crucial for advancing our understanding in this field.

Challenges for Conducting Research during Pandemics. A Narrative Review

Background: Research during pandemics presents unique challenges and opportunities that are crucial for advancing scientific knowledge and improving public health responses. This study's topic is significant due to the unprecedented disruptions caused by pandemics like COVID-19, which have impacted all stages of the research process. This study aims to identify and analyze research challenges during pandemics and propose strategies to overcome these obstacles.Methods: A narrative literature review was conducted, focusing on the challenges of conducting research during pandemics. A comprehensive search strategy was implemented, using databases such as PubMed, Google Scholar, and Web of Science, with keywords including "pandemic," "research challenges," "COVID-19 research," "SARS research," "clinical trials during pandemics," "best practices," and "research strategies." The review covered studies published from 2004 to 2023, including previous pandemics like SARS, H1N1, and COVID-19. Relevant articles were identified through database searches and manual reference list reviews.Discussion: The review revealed multidimensional challenges affecting various stages of the research process. Political, economic, administrative, regulatory, logistical, ethical, and social challenges were identified. Logistical difficulties were prevalent, such as limited access to laboratories and supply chain disruptions. Ethical challenges, including informed consent and data privacy, were magnified during health crises. The mental health impacts on researchers and participants were also significant, with increased anxiety and depression affecting engagement and well-being.Conclusion: The study concludes that while pandemics introduce considerable challenges for conducting research, there are also valuable lessons and best practices that can be applied in future crises. Effective strategies include investing in digital infrastructure, developing flexible ethical frameworks, implementing adaptive clinical trial designs, and supporting mental health for researchers and participants. By learning from the COVID-19 pandemic, the research community can better prepare for and navigate future pandemics, ensuring the continuity and effectiveness of scientific investigations.

A Systematic Review of Adaptive Seamless Clinical Trials for Late-Phase Oncology Development.

Although oncology has seen large scientific and clinical advances over the last decade, it also has one of the lowest success rates for novel agents across therapeutic areas. Adaptive clinical trial design has been a popular option for increasing clinical trial efficiency and the chances of trial success. Seamless clinical trial design are studies in which two or more clinical trial phases are combined into a single study with a pre-specified transition between stages. This integration of phases may enhance efficiency. To understand the precedent for the use of seamless designs, this working group was formed to conduct a comprehensive literature search on seamless clinical trials conducted with confirmatory intent in oncology. Trial design features were extracted into a database and analyzed with descriptive statistics. A literature search identified 68 clinical trials meeting inclusion and exclusion criteria. The most common design feature was a gate on treatment efficacy, where the trial would only proceed to the second stage if sufficient efficacy was observed in the first. The next most common feature was a selection of a dose or treatment regimen. Inferentially and operationally seamless designs were approximately equally represented. Key statistical considerations for seamless phase II/III designs include optimizing design choices by evaluating and comparing operating characteristics across design alternatives as well as showing control of overall Type I error rates. Executing the transition between phases should be evaluated for issues related to accrual, drug production, and procedures to maintain trial integrity. While there are unique statistical, regulatory, and operational considerations for seamless designs they are also uniquely suited to many development settings. These include, for example, addressing dose selection under FDA's Project Optimus and addressing the growing use of biomarkers and personalized medicine approaches in cancer treatment.

Phase 1 Open-Label Study of Omigapil in Patients With LAMA2- or COL6-Related Dystrophy.

Omigapil is a small molecule which inhibits the GAPDH-Siah1-mediated apoptosis pathway. Apoptosis is a pathomechanism underlying the congenital muscular dystrophy subtypes LAMA2-related dystrophy (LAMA2-RD) and COL6-related dystrophy (COL6-RD). Studies of omigapil in the (dyw/dyw) LAMA2-RD mouse model demonstrated improved survival, and studies in the (dy2J/dy2J) LAMA2-RD mouse model and the (Col6a1-/-) COL6-RD mouse model demonstrated decreased apoptosis. A phase 1 open-label, sequential group, ascending oral dose, cohort study of omigapil in patients with LAMA2-RD or COL6-RD ages 5-16 years was performed (1) to establish the pharmacokinetic (PK) profile of omigapil at a range of doses, (2) to evaluate the safety and tolerability of omigapil at a range of doses, and (3) to establish the feasibility of conducting disease-relevant clinical assessments. Patients were enrolled in cohorts of size 4, with each patient receiving 4 weeks of vehicle run-in and 12 weeks of study drug (at daily doses ranging from 0.02 to 0.08 mg/kg). PK data from each cohort were analyzed before each subsequent dosing cohort was enrolled. A novel, adaptive dose-finding method (stochastic approximation with virtual observation recursion) was used to allow for dose escalation/reduction between cohorts based on PK data. Twenty patients were enrolled at the NIH (LAMA2-RD: N = 10; COL6-RD: N = 10). Slightly greater than dose-proportional increases in systemic exposure to omigapil were seen at doses 0.02-0.08 mg/kg/d. The dose which achieved patient exposure within the pre-established target area under the plasma concentration-vs-time curve (AUC0-24h) range was 0.06 mg/kg/d. In general, omigapil was safe and well tolerated. No consistent changes were seen in the disease-relevant clinical assessments during the duration of the study. This study represents the thus far only clinical trial of a therapeutic small molecule for LAMA2-RD and COL6-RD, completed with an adaptive trial design to arrive at dose adjustments. The trial met its primary end point and established that the PK profile of omigapil is suitable for further development in pediatric patients with LAMA2-RD or COL6-RD, the most common forms of congenital muscular dystrophy. While within the short duration of the study disease-relevant clinical assessments did not demonstrate significant changes, this study establishes the feasibility of performing interventional clinical trials in these rare disease patient populations. This study provides Class IV evidence of omigapil in a dose-finding phase 1 study. Clinical Trials NCT01805024.

Adaptive designs in public health: Vaccine and cluster randomized trials go Bayesian.

Clinical trials in public health-particularly those conducted in low- and middle-income countries-often involve communicable and non-communicable diseases with high disease burden and unmet needs. Trials conducted in these regions often are faced with resource limitations, so improving the efficiencies of these trials is critical. Adaptive trial designs have the potential to save trial time and resources and reduce the number of patients receiving ineffective interventions. In this paper, we provide a detailed account of the implementation of vaccine and cluster randomized trials within the framework of Bayesian adaptive trials, with emphasis on computational efficiency and flexibility with regard to stopping rules and allocation ratios. We offer an educated approach to selecting prior distributions and a data-driven empirical Bayes method for plug-in estimates for nuisance parameters.

From ICU Syndromes to ICU Subphenotypes: Consensus Report and Recommendations for Developing Precision Medicine in the ICU.

Critical care uses syndromic definitions to describe patient groups for clinical practice and research. There is growing recognition that a "precision medicine" approach is required and that integrated biologic and physiologic data identify reproducible subpopulations that may respond differently to treatment. This article reviews the current state of the field and considers how to successfully transition to a precision medicine approach. To impact clinical care, identification of subpopulations must do more than differentiate prognosis. It must differentiate response to treatment, ideally by defining subgroups with distinct functional or pathobiological mechanisms (endotypes). There are now multiple examples of reproducible subpopulations of sepsis, acute respiratory distress syndrome, and acute kidney or brain injury described using clinical, physiological, and/or biological data. Many of these subpopulations have demonstrated the potential to define differential treatment response, largely in retrospective studies, and that the same treatment-responsive subpopulations may cross multiple clinical syndromes (treatable traits). To bring about a change in clinical practice, a precision medicine approach must be evaluated in prospective clinical studies requiring novel adaptive trial designs. Several such studies are underway, but there are multiple challenges to be tackled. Such subpopulations must be readily identifiable and be applicable to all critically ill populations around the world. Subdividing clinical syndromes into subpopulations will require large patient numbers. Global collaboration of investigators, clinicians, industry, and patients over many years will therefore be required to transition to a precision medicine approach and ultimately realize treatment advances seen in other medical fields.

Adaptive design trials in eating disorder research: A scoping review.

This scoping review sought to map the breadth of literature on the use of adaptive design trials in eating disorder research. A systematic literature search was conducted in Medline, Scopus, PsycInfo, Emcare, Econlit, CINAHL and ProQuest Dissertations and Theses. Articles were included if they reported on an intervention targeting any type of eating disorder (including anorexia nervosa, bulimia nervosa, binge-eating disorder, and other specified feeding or eating disorders), and employed the use of an adaptive design trial to evaluate the intervention. Two independent reviewers screened citations for inclusion, and data abstraction was performed by one reviewer and verified by a second. We identified five adaptive design trials targeting anorexia nervosa, bulimia nervosa and binge-eating disorder conducted in the USA and Australia. All employed adaptive treatment arm switching based on early response to treatment and identified a priori stopping rules. None of the studies included value of information analysis to guide adaptive design decisions and none included lived experience perspectives. The limited use of adaptive designs in eating disorder trials represents a missed opportunity to improve enrolment targets, attrition rates, treatment outcomes and trial efficiency. We outline the range of adaptive methodologies, how they could be applied to eating disorder research, and the specific operational and statistical considerations relevant to adaptive design trials. Adaptive design trials are increasingly employed as flexible, efficient alternatives to fixed trial designs, but they are not often used in eating disorder research. This first scoping review identified five adaptive design trials targeting anorexia nervosa, bulimia nervosa and binge-eating disorder that employed treatment arm switching adaptive methodology. We make recommendations on the use of adaptive design trials for future eating disorder trials.

Adaptive designs in dermatology clinical trials: Current status and future perspectives.

Current drug development strategies present many challenges that can impede drug approval by regulatory agencies. Alternative study models, such as adaptive trial designs, have recently sparked interest, as they provide a flexible and more efficient approach in conducting clinical trials. Adaptive trial designs offer several potential benefits over traditional randomized controlled trials, which include decrease in costs, reduced clinical development time and limiting exposure of patients to potentially ineffective treatments allowing completion of studies with fewer patients. This article explores the current use of adaptive trial designs in non-oncologic skin diseases and highlights the most common types of adaptive designs used in the field. We also review the operational challenges and statistical considerations associated with such designs and propose clinical development strategies to successfully implement adaptive designs. The article also proposes instances where adaptive trial designs are particularly beneficial, and other situations where they may not be very useful.

Application of seamless phase Ⅱ/Ⅲ design in vaccine clinical trials

The seamless phase Ⅱ/Ⅲ design integrates independent phase Ⅱ and phase Ⅲ clinical trials into a continuous, phased adaptive clinical trial design. Compared with traditional independent phase Ⅱ and phase Ⅲ clinical trials, the seamless design offers significant advantages in accelerating drug or vaccine development and improving clinical trial efficiency. Currently, the application of this design in anti-tumor drug research is becoming increasingly mature, and it is gradually expanding to clinical trials of vaccines, including the 9-valent human papillomavirus vaccine, sabin strain inactivated polio vaccine, and others. This paper aims to clarify the seamless phase Ⅱ/Ⅲ design concept and offer valuable insights into its implementation. It accomplishes this by presenting a clinical trial example featuring a phase Ⅱ/Ⅲ seamless design for a 9-valent human papillomavirus vaccine. The article delves into the specific considerations and potential challenges related to implementing the seamless design, aiming to provide valuable insights for optimizing vaccine clinical trials within our country.