Clinical trial designs to assess treatment effects on glomerular filtration rate decline.

Glomerular filtration rate (GFR) decline is used as surrogate endpoint for kidney failure. Interventions that reduce chronic kidney disease (CKD) progression often exert acute GFR reductions which differ from their long-term benefits and complicate the estimation of long-term benefit. Here, we assessed the utility of two alternative trial designs (wash-out design and active run-in randomized withdrawal design) that attempt to exclude the impact of acute effects. Post-hoc analyses of two clinical trials that characterized the effect of an intervention with acute reductions in GFR were conducted. The two trials included a wash-out period (EMPA-REG Outcome testing empagliflozin vs placebo) or an active run-in period with a randomized withdrawal (SONAR testing atrasentan vs placebo). We compared the drug effect on GFR decline calculated from the first on-treatment visit to the end of treatment (chronic slope in a standard randomized trial design) with GFR change calculated from randomization to end of wash out, or GFR change from treatment-specific baseline GFR values (GFR at start-of-run-in for placebo and end-of-run-in for atrasentan) until end-of-treatment. The effect of empagliflozin versus placebo on chronic GFR slope was 1.72 (95% confidence interval 1.49-1.94) mL/min/1.73 m2/year, similar to total GFR decline from baseline to the end of wash-out period using a linear mixed model 1.64 (1.44-1.85) mL/min/1.73 m2/year). The effect of atrasentan versus placebo on chronic GFR slope was 0.72 (0.32-1.11) mL/min/1.73 m2/year, similar to total slope from a single slope model when estimated from treatment specific baseline GFR values 0.77 (0.39-1.14) mL/min/1.73 m2/year). Statistical power of the two designs outperformed the standard randomized design. Thus, wash-out and active-run-in randomized-withdrawal trial designs are appropriate models to compute treatment effects on GFR decline.

The Role of AI in Drug Discovery.

The emergence of Artificial Intelligence (AI) in drug discovery marks a pivotal shift in pharmaceutical research, blending sophisticated computational techniques with conventional scientific exploration to break through enduring obstacles. This review paper elucidates the multifaceted applications of AI across various stages of drug development, highlighting significant advancements and methodologies. It delves into AI's instrumental role in drug design, polypharmacology, chemical synthesis, drug repurposing, and the prediction of drug properties such as toxicity, bioactivity, and physicochemical characteristics. Despite AI's promising advancements, the paper also addresses the challenges and limitations encountered in the field, including data quality, generalizability, computational demands, and ethical considerations. By offering a comprehensive overview of AI's role in drug discovery, this paper underscores the technology's potential to significantly enhance drug development, while also acknowledging the hurdles that must be overcome to fully realize its benefits.

Methods, design, and initial results of an angiographic core lab from VOYAGER-PAD.

Background: Anatomy is critical in risk stratification and therapeutic decision making in coronary disease. The relationship between anatomy and outcomes is not well described in PAD. We sought to develop an angiographic core lab within the VOYAGER-PAD trial. The current report describes the methods of creating this core lab, its study population, and baseline anatomic variables. Methods: Patients undergoing lower-extremity revascularization for symptomatic PAD were randomized in VOYAGER-PAD. The median follow up was 2.25 years. Events were adjudicated by a blinded Clinical Endpoint Committee. Angiograms were collected from study participants; those with available angiograms formed this core lab cohort. Angiograms were scored for anatomic and flow characteristics by trained reviewers blinded to treatment. Ten percent of angiograms were evaluated independently by two reviewers; inter-rater agreement was assessed. Clinical characteristics and the treatment effect of rivaroxaban were compared between the core lab cohort and noncore lab participants. Anatomic data by segment were analyzed. Results: Of 6564 participants randomized in VOYAGER-PAD, catheter-based angiograms from 1666 patients were obtained for this core lab. Anatomic and flow characteristics were collected across 16 anatomic segments by 15 reviewers. Concordance between reviewers for anatomic and flow variables across segments was 90.5% (24,417/26,968). Clinical characteristics were similar between patients in the core lab and those not included. The effect of rivaroxaban on the primary efficacy and safety outcomes was also similar. Conclusions: The VOYAGER-PAD angiographic core lab provides an opportunity to correlate PAD anatomy with independently adjudicated outcomes and provide insights into therapy for PAD. (ClinicalTrials.gov Identifier: NCT02504216).

Applications of the partial-order continual reassessment method in the early development of treatment combinations.

Combination therapy is increasingly being explored as a promising approach for improving cancer treatment outcomes. However, identifying effective dose combinations in early oncology drug development is challenging due to limited sample sizes in early-phase clinical trials. This task becomes even more complex when multiple agents are being escalated simultaneously, potentially leading to a loss of monotonic toxicity order with respect to the dose. Traditional single-agent trial designs are insufficient for this multi-dimensional problem, necessitating the development and implementation of dose-finding methods specifically designed for drug combinations. While, in practice, approaches to this problem have focused on preselecting combinations with a known toxicity order and applying single-agent designs, this limits the number of combinations considered and may miss promising dose combinations. In recent years, several novel designs have been proposed for exploring partially ordered drug combination spaces with the goal of identifying a maximum tolerated dose combination, based on safety, or an optimal dose combination, based on toxicity and efficacy. However, their implementation in clinical practice remains limited. In this article, we describe the application of the partial order continual reassessment method and its extensions for combination therapies in early-phase clinical trials. We present completed trials that use safety endpoints to identify maximum tolerated dose combinations and adaptively use both safety and efficacy endpoints to determine optimal treatment strategies. We discuss the effectiveness of the partial-order continual reassessment method and its extensions in identifying optimal treatment strategies and provide our experience with executing these novel adaptive designs in practice. By utilizing innovative dose-finding methods, researchers and clinicians can more effectively navigate the challenges of combination therapy development, ultimately improving patient outcomes in the treatment of cancer.

Optimizing the doses of cancer drugs after usual dose finding.

Since the middle of the 20th century, oncology's dose-finding paradigm has been oriented toward identifying a drug's maximum tolerated dose, which is then carried forward into phase 2 and 3 trials and clinical practice. For most modern precision medicines, however, maximum tolerated dose is far greater than the minimum dose needed to achieve maximal benefit, leading to unnecessary side effects. Regulatory change may decrease maximum tolerated dose's predominance by enforcing dose optimization of new drugs. Dozens of already approved cancer drugs require re-evaluation, however, introducing a new methodologic and ethical challenge in cancer clinical trials. In this article, we assess the history and current landscape of cancer drug dose finding. We provide a set of strategic priorities for postapproval dose optimization trials of the future. We discuss ethical considerations for postapproval dose optimization trial design and review three major design strategies for these unique trials that would both adhere to ethical standards and benefit patients and funders. We close with a discussion of financial and reporting considerations in the realm of dose optimization. Taken together, we provide a comprehensive, bird's eye view of the postapproval dose optimization trial landscape and offer our thoughts on the next steps required of methodologies and regulatory and funding regimes.

A comparison of alternative ranking methods in two-stage clinical trials with multiple interventions: An application to the anxiolysis for laceration repair in children trial.

BACKGROUND/AIMS: Multi-arm, multi-stage trials frequently include a standard care to which all interventions are compared. This may increase costs and hinders comparisons among the experimental arms. Furthermore, the standard care may not be evident, particularly when there is a large variation in standard practice. Thus, we aimed to develop an adaptive clinical trial that drops ineffective interventions following an interim analysis before selecting the best intervention at the final stage without requiring a standard care. METHODS: We used Bayesian methods to develop a multi-arm, two-stage adaptive trial and evaluated two different methods for ranking interventions, the probability that each intervention was optimal (Pbest) and using the surface under the cumulative ranking curve (SUCRA), at both the interim and final analysis. The proposed trial design determines the maximum sample size for each intervention using the Average Length Criteria. The interim analysis takes place at approximately half the pre-specified maximum sample size and aims to drop interventions for futility if either Pbest or the SUCRA is below a pre-specified threshold. The final analysis compares all remaining interventions at the maximum sample size to conclude superiority based on either Pbest or the SUCRA. The two ranking methods were compared across 12 scenarios that vary the number of interventions and the assumed differences between the interventions. The thresholds for futility and superiority were chosen to control type 1 error, and then the predictive power and expected sample size were evaluated across scenarios. A trial comparing three interventions that aim to reduce anxiety for children undergoing a laceration repair in the emergency department was then designed, known as the Anxiolysis for Laceration Repair in Children Trial (ALICE) trial. RESULTS: As the number of interventions increases, the SUCRA results in a higher predictive power compared with Pbest. Using Pbest results in a lower expected sample size when there is an effective intervention. Using the Average Length Criterion, the ALICE trial has a maximum sample size for each arm of 100 patients. This sample size results in a 86% and 85% predictive power using Pbest and the SUCRA, respectively. Thus, we chose Pbest as the ranking method for the ALICE trial. CONCLUSION: Bayesian ranking methods can be used in multi-arm, multi-stage trials with no clear control intervention. When more interventions are included, the SUCRA results in a higher power than Pbest. Future work should consider whether other ranking methods may also be relevant for clinical trial design.

Factors Influencing Willingness to Participate in Clinical Studies in Pediatric Anesthesia (FILIPPA): A vignette-based, structured interview study.

BACKGROUND: Informed consent is a relevant backdrop for conducting clinical trials, particularly those involving children. While several factors are known to influence the willingness to consent to pediatric anesthesia studies, the influence of study design on consenting behavior is unknown. AIMS: To quantify the impact of study complexity on willingness to consent to pediatric anesthesia studies. METHODS: We conducted a vignette-based interview study by presenting three hypothetical studies to 106 parents or legal guardians whose children were scheduled to undergo anesthesia. These studies differed in level of complexity and included an example of a prospective observational study, a randomized controlled trial, and a phase-II-pharmacological study. Primary outcome was the willingness to consent, using a 5-point Likert scale ranging from "absolutely consent" to "absolutely decline". Secondary outcomes were the effects of child-related (such as sex, age, previous anesthesia, research exposure) and proxy-related factors. RESULTS: Response probabilities for "absolute consent" were 90.9% [95% CI 85.3-96.5] for the observational study, 48.6% [95% CI 38.3-58.9] for the randomized controlled trial, and 32.7% [95% CI 23.9-41.6] for the phase-II-pharmacological study. Response probabilities for "absolutely decline" were 1.6% [95% CI 0.3-2.8], 14.4% [95% CI 8.3-20.5], and 24.7% [95% CI 16.6-32.7], respectively. Significant effects were found for previous research exposure (OR = 0.486 [95% CI 0.256-0.923], p = .027), older age (OR = 0.963 [95% CI 0.927-0.999], p = .045) and the gender of the parent or legal guardian, as mothers were less willing to consent (OR = 0.234 [95% CI 0.107-0.512], p < .001). CONCLUSIONS: Willingness to consent decreased with increasing level of study complexity. When conducting more complex studies, greater efforts need to be made to increase the enrollment of pediatric patients.

Efficient Study Design and Analysis of Longitudinal Dose-Response Data Using Fractional Polynomials.

Correctly characterising the dose-response relationship and taking the correct dose forward for further study is a critical part of the drug development process. We use optimal design theory to compare different designs and show that using longitudinal data from all available timepoints in a continuous-time dose-response model can substantially increase the efficiency of estimation of the dose-response compared to a single timepoint model. We give theoretical results to calculate the efficiency gains for a large class of these models. For example, a linearly growing Emax dose-response in a population with a between/within-patient variance ratio ranging from 0.1 to 1 measured at six visits can be estimated with between 1.43 and 2.22 times relative efficiency gain, or equivalently, with 30% to a 55% reduced sample size, compared to a single model of the final timepoint. Fractional polynomials are a flexible way to incorporate data from repeated measurements, increasing precision without imposing strong constraints. Longitudinal dose-response models using two fractional polynomial terms are robust to mis-specification of the true longitudinal process while maintaining, often large, efficiency gains. These models have applications for characterising the dose-response at interim or final analyses.

Comparative review of novel model-assisted designs for phase I/II clinical trials.

In recent years, both model-based and model-assisted designs have emerged to efficiently determine the optimal biological dose (OBD) in phase I/II trials for immunotherapy and targeted cellular agents. Model-based designs necessitate repeated model fitting and computationally intensive posterior sampling for each dose-assignment decision, limiting their practical application in real trials. On the other hand, model-assisted designs employ simple statistical models and facilitate the precalculation of a decision table for use throughout the trial, eliminating the need for repeated model fitting. Due to their simplicity and transparency, model-assisted designs are often preferred in phase I/II trials. In this paper, we systematically evaluate and compare the operating characteristics of several recent model-assisted phase I/II designs, including TEPI, PRINTE, Joint i3+3, BOIN-ET, STEIN, uTPI, and BOIN12, in addition to the well-known model-based EffTox design, using comprehensive numerical simulations. To ensure an unbiased comparison, we generated 10,000 dosing scenarios using a random scenario generation algorithm for each predetermined OBD location. We thoroughly assess various performance metrics, such as the selection percentages, average patient allocation to OBD, and overdose percentages across the eight designs. Based on these assessments, we offer design recommendations tailored to different objectives, sample sizes, and starting dose locations.

Contributions of the ADNI Biostatistics Core.

The goal of the Biostatistics Core of the Alzheimer's Disease Neuroimaging Initiative (ADNI) has been to ensure that sound study designs and statistical methods are used to meet the overall goals of ADNI. We have supported the creation of a well-validated and well-curated longitudinal database of clinical and biomarker information on ADNI participants and helped to make this accessible and usable for researchers. We have developed a statistical methodology for characterizing the trajectories of clinical and biomarker change for ADNI participants across the spectrum from cognitively normal to dementia, including multivariate patterns and evidence for heterogeneity in cognitive aging. We have applied these methods and adapted them to improve clinical trial design. ADNI-4 will offer us a chance to help extend these efforts to a more diverse cohort with an even richer panel of biomarker data to support better knowledge of and treatment for Alzheimer's disease and related dementias. HIGHLIGHTS: The Alzheimer's Disease Neuroimaging Initiative (ADNI) Biostatistics Core provides study design and analytic support to ADNI investigators. Core members develop and apply novel statistical methodology to work with ADNI data and support clinical trial design. The Core contributes to the standardization, validation, and harmonization of biomarker data. The Core serves as a resource to the wider research community to address questions related to the data and study as a whole.

Protocol-in-a-Day Workshop: Expediting IRB Approval for Junior and Senior Faculty.

Delays in research protocol development may be a single factor that hinders the career progression of academic faculty. Structured educational guidance during this phase proves crucial in mitigating setbacks in Institutional Review Board (IRB) approval and expediting trial implementation. To address this, the Protocol-in-a-Day (PIAD) workshop, a comprehensive 1-day event involving members from six critical facets of RO clinical trial implementation, was established, offering significant input to individual protocols. Efficacy and satisfaction of the PIAD workshop were assessed through a 5-question survey and the average time from submission to IRB initial approval. The normality of the data was analyzed using the Shapiro-Wilk Test. Nonparametric data was analyzed using a Mann-Whitney U test for significance. A total of 18 protocols that went through the PIAD workshop were activated. The mean time to IRB approval for protocols that went through PIAD was 39.8 days compared to 58.4 days for those that did not go through the PIAD workshop. Based on survey results, 100% of PIAD participants said the PIAD workshop was useful and 94% of participants stated that the PIAD workshop improved the overall quality of their protocol. Participant surveys further highlighted substantial improvements in trial quality, language, and statistical design and revealed that all participants found the workshop helpful. Therefore, both junior and senior faculty benefitted from this educational program during protocol development, as both groups demonstrated shorter times to IRB approval than non-participants. This acceleration not only fosters efficient trial implementation but also supports academic faculty in their career development.

Estimating vaccine efficacy during open-label follow-up of COVID-19 vaccine trials based on population-level surveillance data.

While rapid development and roll out of COVID-19 vaccines is necessary in a pandemic, the process limits the ability of clinical trials to assess longer-term vaccine efficacy. We leveraged COVID-19 surveillance data in the U.S. to evaluate vaccine efficacy in U.S. Government-funded COVID-19 vaccine efficacy trials with a three-step estimation process. First, we used a compartmental epidemiological model informed by county-level surveillance data, a "population model", to estimate SARS-CoV-2 incidence among the unvaccinated. Second, a "cohort model" was used to adjust the population SARS-CoV-2 incidence to the vaccine trial cohort, taking into account individual participant characteristics and the difference between SARS-CoV-2 infection and COVID-19 disease. Third, we fit a regression model estimating the offset between the cohort-model-based COVID-19 incidence in the unvaccinated with the placebo-group COVID-19 incidence in the trial during blinded follow-up. Counterfactual placebo COVID-19 incidence was estimated during open-label follow-up by adjusting the cohort-model-based incidence rate by the estimated offset. Vaccine efficacy during open-label follow-up was estimated by contrasting the vaccine group COVID-19 incidence with the counterfactual placebo COVID-19 incidence. We documented good performance of the methodology in a simulation study. We also applied the methodology to estimate vaccine efficacy for the two-dose AZD1222 COVID-19 vaccine using data from the phase 3 U.S. trial (ClinicalTrials.gov # NCT04516746). We estimated AZD1222 vaccine efficacy of 59.1% (95% uncertainty interval (UI): 40.4%-74.3%) in April, 2021 (mean 106 days post-second dose), which reduced to 35.7% (95% UI: 15.0%-51.7%) in July, 2021 (mean 198 days post-second-dose). We developed and evaluated a methodology for estimating longer-term vaccine efficacy. This methodology could be applied to estimating counterfactual placebo incidence for future placebo-controlled vaccine efficacy trials of emerging pathogens with early termination of blinded follow-up, to active-controlled or uncontrolled COVID-19 vaccine efficacy trials, and to other clinical endpoints influenced by vaccination.

Perispinal etanercept stroke trial design: PESTO and beyond.

INTRODUCTION: Perispinal etanercept (PSE) is an innovative treatment designed to improve stroke recovery by addressing chronic post-stroke neuroinflammation. Basic science evidence, randomized clinical trial (RCT) evidence and 14 years of favorable clinical experience support the use of PSE to treat chronic stroke. This article provides guidance for the design of future PSE RCTs in accordance with current FDA recommendations. AREAS COVERED: Scientific background and essential elements of PSE RCT design. EXPERT OPINION: Intimate familiarity with PSE, its novel method of drug delivery, and the characteristics of ideal enriched study populations are necessary for those designing future PSE stroke trials. The design elements needed to enable a PSE RCT to generate valid results include a suitable research question; a homogeneous study population selected using a prospective enrichment strategy; a primary outcome measure responsive to the neurological improvements that result from PSE; trialists with expertise in perispinal delivery; optimal etanercept dosing; and steps taken to minimize the number of placebo responders. RCTs failing to incorporate these elements, such as the PESTO trial, are incapable of reaching reliable conclusions regarding PSE efficacy. SF-36 has not been validated in PSE trials and is unsuitable for use as a primary outcome measure in PSE RCTs.

Advancing Transthyretin Amyloidosis Drug Development in an Evolving Treatment Landscape: Amyloidosis Forum Meeting Proceedings.

INTRODUCTION: Hereditary transthyretin amyloidosis (ATTRv, also referred to as hATTR; ORPHA 271861) and wild-type ATTR amyloidosis (ATTRwt; ORPHA 330001) are rare, progressive, systemic protein misfolding disorders with heterogeneous clinical presentations. ATTRv and ATTRwt amyloidosis are characterized by the deposition of amyloid fibrils in multiple organs including the heart, nerves, eyes, and soft tissues. The management of ATTR amyloidosis is complex because of its multisystemic nature and progression despite available treatment options. Morbidity is high and there are many unmet medical needs for patients. While contemporary ATTR amyloidosis cohorts are diagnosed earlier, have lower risk disease and lower mortality compared with the previous era, these advances coupled with the emergence of effective disease-modifying therapies have confounded the design of future prospective clinical trials and interpretation of historical control data. MAIN BODY: The Amyloidosis Forum is a public-private partnership between the US Food and Drug Administration Center for Drug Evaluation and Research and the nonprofit Amyloidosis Research Consortium ( www.arci.org ). This article summarizes proceedings from the 21 June 2023 Amyloidosis Forum on advancing drug development in ATTR amyloidosis in an evolving treatment landscape. The Forum focused on elements of clinical trial design to address these challenges and discussed their strengths and weaknesses from multiple stakeholder perspectives (i.e., patient, sponsor, statistician, clinician, and regulatory authorities). CONCLUSION: Given rapid evolution of natural history in ATTR amyloidosis, the utility of historical control data is limited. Leveraging contemporary real-world data is essential for clinical trial design. Evidence generation from clinical trials should address clinically relevant questions. Key factors in successful trial design must be informed by up-to-date data on natural history, prognostic factors, clinically meaningful thresholds, and sharing available clinical trial data. The Amyloidosis Forum includes the community of patients with ATTR amyloidosis, the physicians who treat them, and the sponsors and regulators who collectively stand ready to support further studies in order to develop novel effective therapies.

Intramuscular ketamine vs. midazolam for rapid risk-reduction in suicidal, depressed emergency patients: Clinical trial design and rationale.

Emergency department (ED) visits for suicidal ideation or behavior have been increasing in all age groups, particularly younger adults. A rapid-acting treatment to reduce suicidal thinking, adapted for ED use, is needed. Previous studies have shown a single dose of ketamine can improve depression and suicidal ideation within hours. However, most studies used 40 min intravenous infusions which can be impractical in a psychiatric ED. The ER-Ketamine study we describe here is a randomized midazolam-controlled clinical trial (RCT; NCT04640636) testing intramuscular (IM) ketamine's feasibility, safety, and effectiveness to rapidly reduce suicidal ideation and depression in a psychiatric ED. A pre-injection phase involves screening, informed consent, eligibility confirmation, and baseline assessment of suicidal ideation, depression, and comorbidities. The randomized double-blind IM injection is administered in the ED under research staff supervision, vital sign monitoring, pharmacokinetic blood sampling, and clinical assessments. The post-injection phase occurs on a psychiatric inpatient unit with follow-up research assessments through four weeks post-discharge. Outcome measures are feasibility, safety, and effects on suicidal ideation and depression at 24 h post-injection, and through follow-up. The target sample is N = 90 adults in a major depressive episode, assessed by ED clinicians as warranting hospitalization for suicide risk. Here we report design, rationale, and preliminary feasibility and safety for this ongoing study. Demographics of the 53 participants (ages 18 to 65 years) randomized to date suggest a diverse sample tending towards younger adults.

Sensitivity Analyses of Clinical Trial Designs: Selecting Scenarios and Summarizing Operating Characteristics.

The use of simulation-based sensitivity analyses is fundamental for evaluating and comparing candidate designs of future clinical trials. In this context, sensitivity analyses are especially useful to assess the dependence of important design operating characteristics with respect to various unknown parameters. Typical examples of operating characteristics include the likelihood of detecting treatment effects and the average study duration, which depend on parameters that are unknown until after the onset of the clinical study, such as the distributions of the primary outcomes and patient profiles. Two crucial components of sensitivity analyses are (i) the choice of a set of plausible simulation scenarios and (ii) the list of operating characteristics of interest. We propose a new approach for choosing the set of scenarios to be included in a sensitivity analysis. We maximize a utility criterion that formalizes whether a specific set of sensitivity scenarios is adequate to summarize how the operating characteristics of the trial design vary across plausible values of the unknown parameters. Then, we use optimization techniques to select the best set of simulation scenarios (according to the criteria specified by the investigator) to exemplify the operating characteristics of the trial design. We illustrate our proposal in three trial designs.

Adult neuro-oncology trials in the United States over 5 decades: Analysis of trials completion rate to guide the path forward.

Background: Clinical trials are important to close the gap between therapeutic unmet needs and scientific advances in neuro-oncology. This study analyzes the landscape of neuro-oncology trials to identify completion rates and guide strategies for the path forward. Methods: US-registered adult neuro-oncology clinical trials were extracted from www.clinicaltrials.gov (1966-2019), including funding source, trial type, scope, phase, and subjects' demographics. Completed trials defined as those that had completed participants' examinations or intervention administration for the purpose of the final collection of data for the primary outcome were dichotomized against those that failed to reach completion. Univariate and multivariate analyses were used to detect differences across factors comparing the last 2 decades (2000-2009, 2010-2019). Results: Our search yielded 4522 trials, of which 1257 are eligible for this study. In 25 US states, neuro-oncology trial availability is <0.85/100,000 population. Comparing the past 2 decades, trial completion rate decreased from 88% to 64% (P < .001) and National Institutes of Health funding decreased from 47% to 24% (P < .001). Inclusion of subjects >65-year-old and women increased, while inclusion of Hispanic subjects decreased (P < .001). The top 2 reasons for lack of completion included accrual and operational difficulties. A larger proportion of women, non-Hispanic subjects, and older adults were enrolled in completed trials than in those that failed completion. Conclusions: Our study is the first report on the neuro-oncology clinical trial landscape in the United States and supports the development of strategies to further improve access to these trials. Additionally, attention is needed to identify and modify other factors contributing to lack of completion.

The challenge of running trials in advanced angiosarcoma: A systematic review of the literature from EORTC/STBSG to guide the development of angiosarcoma-specific trials.

INTRODUCTION: While available systemic treatments have modest long term efficacy in advanced angiosarcoma, immunotherapy represents an interesting new therapeutic opportunity. To establish its benefit, it is required to conduct a clinical trial assessing its efficacy and toxicity compared to standard treatments. MATERIAL AND METHODS: This is a literature review from PubMed search. RESULTS: Several systemic treatments (chemotherapy and TKI) are currently used in advanced angiosarcoma with ORR ranging from 12.5 to 68 % and PFS from 2 to 7 months. However, few randomized trials, mainly phase II, has been conducted to compare these treatments. While most centers propose doxorubicin containing regimens or paclitaxel in 1st or 2nd line, a high heterogeneity of regimens administered in this setting is observed even across sarcoma specialized centers with no consensual standard treatment. Encouraging signals of immunotherapy activity have been reported in angiosarcoma from several retrospective and phase II studies assessing anti-PD1 either alone or in combination with anti CTLA4 or TKI. Although cutaneous and head and neck location seems to benefit more from immunotherapy, response may be observed in any angiosarcoma subtype. In sarcoma in general and AS in particular, no biomarker has been clearly established to predict the efficacy of immunotherapy: high tumor mutational burden and presence of tertiary lymphoid structures are under assessment. DISCUSSION: Even essential, developing a randomized clinical trial in AS struggles with the heterogeneity of the disease, the lack of consensual standard regimen, the uncertainty on optimal immunotherapy administration and the absence of established predictive biomarkers. CONCLUSION: International collaboration is essential to run randomized trial in advanced AS and asses the efficacy of immune therapy in this rare and heterogeneous disease.

Critical Illness Outside the Intensive Care Unit: Research Challenges in Emergency and Prehospital Settings.

Patients with acute critical illness require prompt interventions, yet high-quality evidence supporting many investigations and treatments is lacking. Clinical research in this setting is challenging due to the need for immediate treatment and the inability of patients to provide informed consent. Attempts to obtain consent from surrogate decision-makers can be intrusive and lead to unacceptable delays to treatment. These problems may be overcome by pragmatic approaches to study design and the use of supervised waivers of consent, which is ethical and appropriate in situations where there is high risk of poor outcome and a paucity of proven effective treatment.