Optimal Trial Design Research Articles

Disease-free survival as a surrogate endpoint for overall survival in adults with early-stage pancreatic cancer: A trial-level surrogacy analysis.

708 Background: In early-stage cancer, overall survival (OS) is the gold-standard endpoint for assessing therapeutic efficacy. However, long follow-up periods make OS challenging and costly to measure in clinical trials. Disease-free survival (DFS), which captures the time to recurrence or death, has been proposed as a surrogate endpoint for OS in early-stage pancreatic cancer (PC). This surrogacy relationship could allow for a more rapid assessment of treatment benefits, but its validity requires robust analysis across clinical trials. Understanding the trial-level correlation between DFS and OS is crucial to optimizing trial design and grant accelerated drug approval. Methods: We conducted a systematic search of Embase, PubMed, and Cochrane databases through August 2024, identifying randomized controlled trials (RCTs) that evaluated adjuvant or neoadjuvant systemic therapies in adult patients with PC. Included studies reported both OS and DFS outcomes. Hazard ratios for DFS (HR_DFS) were analyzed as surrogate endpoints for OS (HR_OS). Statistical analyses utilized linear regression models, weighted by trial sample sizes, to determine the strength of the correlation. Surrogacy strength was categorized by the coefficient of determination (R²), with R² ≥ 0.7 indicating a strong correlation, 0.69–0.5 a moderate correlation, and values < 0.5 indicating weak correlation. Results: Out of 3,048 studies screened, 30 RCTs met the inclusion criteria, comprising 5,275 patients in adjuvant therapy trials and 557 patients in neoadjuvant therapy trials. The median follow-up duration ranged from 16 to 53.5 months, with 12 phase II trials included. Overall, DFS showed strong surrogacy for OS in adjuvant trials (R² = 0.70; 95% CI, 0.50–0.91). Subgroup analysis indicated strong correlation in phase III trials (R² = 0.71; 95% CI, 0.46–0.96) and moderate correlation in phase II trials (R² = 0.67; 95% CI, 0.21–1.00). The strongest surrogacy was observed in studies incorporating adjuvant radiation therapy (R² = 0.81; 95% CI, 0.52–1.00). In the analysis of neoadjuvant trials, HR-DFS also presented strong correlation with OS (R² = 0.90; 95% CI, 0.56–1.00). Conclusions: There is a strong trial-level correlation between DFS and OS in both adjuvant and neoadjuvant therapy trials for early-stage pancreatic cancer, particularly in phase III studies and trials incorporating adjuvant radiation therapy. However, the strength of this surrogacy varies depending on the treatment setting and trial design.

“Fact checking” with the patient community: An industry-advocacy collaboration for optimizing trial design

“Fact checking” with the patient community: An industry-advocacy collaboration for optimizing trial design

Phase I trial of ATR inhibitor elimusertib with FOLFIRI in advanced or metastatic gastrointestinal malignancies (ETCTN 10406).

ATR is an apical DDR kinase activated at damaged replication forks. Elimusertib is an oral ATR inhibitor and potentiates irinotecan in human colorectal cancer models. To establish dose and tolerability of elimusertib with FOLFIRI, a Bayesian Optimal Interval trial design was pursued. Starting elimusertib dose was 20mg BID days 1, 2, 15 and 16 every 28-day cycle, combined with irinotecan (150mg/m2) and 5-FU (2000 mg/m2). The trial was stopped after 10 accruals, with four DLT across 4 dose levels including grade 3 febrile neutropenia, mucositis, nausea, vomiting and grade 4 neutropenia. The most common grade 3/4 adverse events were neutropenia, leukopenia, lymphopenia and mucositis. Based on significant toxicities the trial was stopped. PK data for 5-FU and irinotecan were unremarkable and did not account for DLTs. Among the six response evaluable patients, four had stable disease as their best response. Median PFS was 7 months. A first case of ATRi chemotherapy combination related AML (t-AML) was observed. The combination of elimusertib with FOLFIRI was associated with intolerable toxicity. Combination of ATR kinases with chemotherapies that target DNA replication may be associated with significant myelotoxicity. Ongoing ATRi trials should monitor for t-AML. NCT04535401.

Implementing sensor-based digital health technologies in clinical trials: Key considerations from the eCOA Consortium.

The increased use of sensor-based digital health technologies (DHTs) in clinical trials brought to light concerns about implementation practices that might introduce burden on trial participants, resulting in suboptimal compliance and become an additional complicating factor in clinical trial conduct. These concerns may contribute to the lower-than-anticipated uptake of DHT deployment and data use for regulatory decision-making, despite well-articulated benefits. The Electronic Clinical Outcome Assessment (eCOA) Consortium gathered collective experience on deploying sensor-based DHTs and supplemented this with relevant literature focusing on mechanisms that may enhance participant compliance. The process for DHT implementation starts with identifying a clinical concept of interest followed by a digital measure selection, defining active or passive data capture and their sources, the number of sensors with respective body location, plus the duration and frequency of use in the context of perceived participant burden. Roundtable discussions among patient groups, physicians, and technology providers prior to protocol development can be very impactful for optimizing trial design. While diversity and inclusion are essential for any clinical trial, patient populations should be considered carefully in the context of trial-specific aims, requirements, and anticipated patient burden. Minimizing site burden includes assessment of training, research engagement, and logistical burden which needs to be triaged differently for early and late-stage clinical trials. Additional considerations include sharing trial results with study participants and leveraging publicly available data for compliance modeling. To the best of our knowledge, this report provides holistic considerations for sensor-based DHT implementation that may optimize participant compliance.

Use of Nanotechnology in Cosmetics and Cosmeceuticals

Nanotechnology has revolutionized the cosmetic and cosmeceutical industries by enhancing product efficacy and enabling innovative formulations. This review paper delves into the various applications of nanotechnology in skincare and cosmetic products, highlighting its ability to improve the delivery of active ingredients, provide enhanced protection, and increase the bioavailability of beneficial compounds. Nanomaterials such as liposomes, nanoemulsions, nanocapsules, and solid lipid nanoparticles are now extensively utilized for their superior skin penetration capabilities, ensuring targeted delivery and prolonged effects. The paper explores the potential benefits and safety concerns associated with these nano-based cosmetic products. It also provides a comprehensive analysis of recent advancements, regulatory considerations, and future trends in the field, emphasizing the need for further research to fully understand the long-term implications of nanotechnology in cosmetics and cosmeceuticals. This review aims to provide a critical overview for researchers and industry professionals interested in the evolving landscape of nanotechnology in beauty and skincare. Findings Our research highlights the transformative impact of Artificial Intelligence (AI) and Machine Learning (ML) on the drug discovery and development process, particularly in enhancing efficiency and precision from initial screening to clinical trials. The integration of AI/ML has shown significant advancements in early-stage drug discovery, where data-driven algorithms enable rapid identification of potential drug candidates, reducing reliance on traditional, labor-intensive methods. In the drug design and optimization phase, AI-driven predictive models have streamlined the process, minimizing the need for extensive physical testing by accurately simulating drug interactions and predicting possible side effects. Additionally, AI and ML are revolutionizing clinical trials by optimizing trial design, improving patient recruitment and retention, and enhancing real-time data monitoring, leading to faster and more reliable trial outcomes. These technologies also support personalized medicine approaches and have proven essential in reducing both the time and cost associated with bringing new therapies to market. Overall, our findings underscore the critical role of AI and ML in reshaping the pharmaceutical landscape, making drug development faster, more cost-effective, and ultimately, more successful in delivering effective treatments to patients.

AI in Healthcare: Breaking New Ground in the Management and Treatment of Cancer

Artificial intelligence (AI) is revolutionizing cancer treatment by enhancing patient management, diagnosis, and therapy. Through advanced AI algorithms, early diagnosis of tumors is made more accurate by detecting subtle abnormalities that might be missed by the human eye. This early detection is critical for successful treatment and better patient outcomes. The objective of this research is to evaluate the impact of AI on improving cancer diagnosis, treatment customization, and drug development processes. The findings reveal that AI significantly accelerates the drug discovery process by analyzing large datasets to identify potential candidates, thus reducing the time and costs associated with developing new treatments. Furthermore, AI enhances clinical trials by improving patient recruitment and optimizing trial design, leading to higher success rates. AI-driven telemedicine solutions also help bridge healthcare gaps in underserved areas, ensuring more equitable access to cancer treatment. The research’s implications suggest that, despite challenges such as data security and the need for improved infrastructure, AI holds great potential to further individualize, streamline, and expand the availability of cancer care. Future advancements are likely to make cancer treatment even more precise, effective, and accessible worldwide.

The Potential of Disease Progression Modeling to Advance Clinical Development and Decision Making.

While some model-informed drug development frameworks are well recognized as enabling clinical trials, the value of disease progression modeling (DPM) in impacting medical product development has yet to be fully realized. The Clinical Trials Transformation Initiative assembled a diverse project team from across the patient, academic, regulatory, and industry sectors of practice to advance the use of DPM for decision making in clinical trials and medical product development. This team conducted a scoping review to explore current applications of DPM and convened a multi-stakeholder expert meeting to discuss its value in medical product development. In this article, we present the scoping review and expert meeting output and propose key questions that medical product developers and regulators may use to inform clinical development strategy, appreciate the therapeutic context and endpoint selection, and optimize trial design with disease progression models. By expanding awareness of the unique value of DPM, this article does not aim to be technical in nature but rather aims to highlight the potential of DPM to improve the quality and efficiency of medical product development.

Transforming Drug Discovery: The Impact of Artificial Intelligence and Machine Learning from Initial Screening to Clinical Trials

Abstract: The landscape of drug discovery is undergoing a profound transformation driven by the integration of Artificial Intelligence (AI) and Machine Learning (ML) technologies. This paper explores the impact of these advancements on various stages of the drug discovery process, from initial screening to clinical trials. Initially, AI and ML are revolutionizing the early stages of drug discovery by enhancing the efficiency of target identification and lead compound screening. Traditional methods, which often rely on labor-intensive processes and high costs, are being supplemented with AI-driven algorithms that analyze vast datasets to identify potential drug targets and predict the biological activity of compounds with unprecedented accuracy. In the drug design and optimization phase, ML models facilitate the prediction of drug interactions and side effects, thus accelerating the development of safer and more effective therapeutics. Advanced simulations and predictive models reduce the reliance on experimental trials, thereby streamlining the development pipeline. The clinical trials phase also benefits significantly from AI and ML. These technologies improve patient stratification by identifying suitable candidates based on genetic and clinical data, optimizing trial designs, and predicting patient responses to treatment. This not only enhances the efficiency of clinical trials but also increases the likelihood of successful outcome. Findings Our research highlights the transformative impact of Artificial Intelligence (AI) and Machine Learning (ML) on the drug discovery and development process, particularly in enhancing efficiency and precision from initial screening to clinical trials. The integration of AI/ML has shown significant advancements in early-stage drug discovery, where data-driven algorithms enable rapid identification of potential drug candidates, reducing reliance on traditional, labor-intensive methods. In the drug design and optimization phase, AI-driven predictive models have streamlined the process, minimizing the need for extensive physical testing by accurately simulating drug interactions and predicting possible side effects. Additionally, AI and ML are revolutionizing clinical trials by optimizing trial design, improving patient recruitment and retention, and enhancing real-time data monitoring, leading to faster and more reliable trial outcomes. These technologies also support personalized medicine approaches and have proven essential in reducing both the time and cost associated with bringing new therapies to market. Overall, our findings underscore the critical role of AI and ML in reshaping the pharmaceutical landscape, making drug development faster, more cost-effective, and ultimately, more successful in delivering effective treatments to patients.

The Alzheimer's Disease Neuroimaging Initiative Clinical Core.

The Alzheimer's Disease Neuroimaging Initiative (ADNI) Clinical Core is responsible for coordination of all clinical activities at the ADNI sites, including project management, regulatory oversight, and site management and monitoring, as well as the collection of all clinical data and management of all study data. The Clinical Core is also charged with determining the clinical classifications and criteria for enrollment in evolving AD trials and enabling the ongoing characterization of the cross-sectional features and longitudinal trajectories of the ADNI cohorts with application of these findings to optimal clinical trial designs. More than 2400 individuals have been enrolled in the cohorts since the inception of ADNI, facilitating refinement of our understanding of the AD trajectory and allowing academic and industry investigators to model therapeutic trials across the disease spectrum from the presymptomatic stage through dementia. HIGHLIGHTS: Since 2004, the Alzheimer's Disease Neuroimaging Initiative (ADNI) Clinical Core has overseen the enrollment of > 2400 participants with mild cognitive impairment, mild Alzheimer's disease (AD) dementia, and normal cognition. The longitudinal dataset has elucidated the full cognitive and clinical trajectory of AD from its presymptomatic stage through the onset of dementia. The ADNI data have supported the design of most major trials in the field.

An open-label study to explore the optimal design of CYP3A drug-drug interaction clinical trials in healthy Chinese people.

A drug-drug interaction (DDI) trial of cytochrome P450 3A (CYP3A) is a necessary part of early-phase trials of drugs mainly metabolized by this enzyme, but CYP3A DDI clinical trials do not have a standard design, especially for Chinese people. We aimed to offer specific recommendations for CYP3A DDI clinical trial design. This was an open, three-cycle, self-controlled study. Healthy subjects were given different administration strategies of CYP3A4 perpetrators. In each cycle, blood samples were collected before and within 24 h after the administration of midazolam, the CYP3A indicator substrate. The plasma concentrationsof midazolam and 1-hydroxymidazolam was obtained using liquid chromatography tandem mass spectrometry assay. For CYP3A inhibition, itraconazole exposure with a loading dose could increase the exposure of midazolam by 3.21-fold based on maximum plasma concentration (Cmax), 8.37-fold based on area under the curve Pharmacology Research & Perspectives for review only from zero to the time point (AUC0-t), and 11.22-fold based on area under the curve from zero to infinity (AUC0-∞). The data were similar for itraconazole pretreatment without a loading dose. For CYP3A induction, the exposure of rifampin for 7 days decreased the plasma concentration of midazolam ~0.27-fold based on Cmax, ~0.18-fold based on AUC0-t, and ~0.18-fold based on AUC0-∞. Midazolam exposure did not significantly change when the pretreatment of rifampin increased to 14 days. This study showed that itraconazole pretreatment for 3 days without a loading dose was enough for CYP3A inhibition, and pretreatment with rifampin for 7 days could induce near-maximal CYP3A levels.

Evolution in coeliac disease diagnosis and management.

The traditional gut-centric view of coeliac disease is evolving as immune and genetic insights underscore the central importance of a systemic, T cell immune response to gluten in disease pathogenesis. As the field increasingly recognize the limitations of small intestinal histology as the diagnostic standard, data supporting the accuracy of an immune (serologic) diagnosis of coeliac disease - well demonstrated in children - are growing for adults. Novel biomarkers such as interleukin-2 that identify the gluten-specific T cell demonstrate high sensitivity and specificity for coeliac disease and offer the potential for a diagnostic approach that avoids the need for gluten challenge. Asymptomatic disease and manifestations outside the gut pose considerable challenges for diagnosis using a case-finding strategy and enthusiasm for population screening is growing. The gluten-free diet remains a highly restrictive treatment and there is a paucity of controlled data to inform a safe gluten intake threshold. Ongoing symptoms and enteropathy are common and require systematic evaluation. Slowly-responsive disease is prevalent in the older patient diagnosed with coeliac disease, and super-sensitivity to gluten is an emerging concept that may explain many cases of nonresponsive disease. While there is great interest in developing novel therapies for coeliac disease, no drug has yet been registered. Efficacy studies are generally assessing drugs in patients with treated coeliac disease who undergo gluten challenge or in patients with nonresponsive disease; however, substantial questions remain around specific endpoints relevant for patients, clinicians and regulatory agencies and optimal trial design. Novel immune tools are providing informative readouts for clinical trials and are now shaping their design.

Biomarker-Driven Oncology Trial Design and Subgroup Characterization: Challenges and Potential Solutions.

In oncology drug development, using biomarkers to select a study population more likely to benefit from a therapeutic effect is critical to increase the efficiency of a clinical trial in demonstrating effectiveness. This perspective delves into therapeutic product approvals that were tested in pivotal trials with all-comers populations, but ultimately received US Food and Drug Administration approval for use within specific patient subgroups identified by biomarkers. Despite initial designs for efficacy and safety assessments in overall populations, a favorable benefit-risk assessment was primarily established in biomarker-positive subgroups. Analyzing these cases, we summarize key considerations pivotal to totality of evidence for regulatory benefit-risk assessments for biomarker-defined subgroup versus all-comers approvals, including biological and clinical rationales, biomarker prevalence, safety data, overall trial design, and subgroup efficacy characterization. Furthermore, a decision tree is proposed to guide optimal clinical trial design, delineating between patient enrichment and stratification, accounting for key factors including biological and clinical rationale, marker type (discreate or continuous), prevalence, assay readiness, and turnaround times for marker assessment. Finally, a recommended approach for subgroup characterization involves prespecifying magnitude of improvement that would be considered clinically meaningful in the biomarker-negative subgroup, which can be supplemented with methodologies such as Bayesian to incorporate evidence from similar studies when available. In summary, this perspective underscores the importance of clinical trial innovations, statistical methodologies and regulatory considerations, to optimize biomarker-driven drug development for patients with cancer.

Gene therapies for CMT neuropathies: from the bench to the clinic.

Charcot-Marie-Tooth (CMT) neuropathies are rare, genetically heterogeneous and progressive diseases for which there are no approved treatments and their management remains mostly supportive and symptomatic. This review is intended to provide an update on recent developments in gene therapies for different CMT neuropathies. Increasing knowledge of disease pathomechanisms underlying several CMT types has facilitated the development of promising viral and nonviral gene therapy approaches. Some of these therapies are currently approaching the crucial step of moving from the bench to the clinic, having passed the proof-of-concept stage in rodent models and some also in larger animals. However, questions of optimal delivery route and dose, off-target effects, and possible payload toxicity remain to be clarified for several of these approaches. Furthermore, limited resources, the rarity of most CMT subtypes, and issues of safety and regulatory requirements, create the need for consensus guidelines and optimal clinical trial design. Promising gene therapies have been developed for several CMT neuropathies, with proof-of-principle demonstrated in relevant disease models. Advantages and drawbacks of each approach are discussed and remaining challenges are highlighted. Furthermore, we suggest important parameters that should be considered in order to successfully translate them into the clinic.

Leveraging the pleural space for anticancer therapies in pleural mesothelioma.

Most patients with pleural mesothelioma (PM) present with symptomatic pleural effusion. In some patients, PM is only detectable on the pleural surfaces, providing a strong rationale for intrapleural anticancer therapy. In modern prospective studies involving expert radiological staging and specialist multidisciplinary teams, the population incidence of stage I PM (an approximate surrogate of pleura-only PM) is higher than in historical retrospective series. In this Viewpoint, we advocate for the expansion of intrapleural trials to serve these patients, given the paucity of data supporting licensed systemic therapies in this setting and the uncertainties involved in surgical therapy. We begin by reviewing the unique anatomical and physiological features of the PM-bearing pleural space, before critically appraising the evidence for systemic therapies in stage I PM and previous intrapleural PM trials. We conclude with a summary of key challenges and potential solutions, including optimal trial designs, repurposing of indwelling pleural catheters, and new technologies.

Biomarkers for Precision Patient Selection in Cancer Therapy Approvals in the US, from 2011 to 2023.

During the period of 2011-2023, the US Food and Drug Administration (US FDA) granted 139 accelerated and 329 regular approvals for 86 and 152 cancer therapeutic products, respectively. The percentage of approvals for a biomarker-defined population was numerically higher in accelerated approvals in comparison to regular approvals, that is, 48% vs. 40%. From 2011-2016 to 2017-2023, there was an increasing number of approvals with biomarker-defined populations in lung and breast cancers, serving as the primary driver for the overall increase in the percentage of approvals for biomarker-defined populations in solid tumors relative to hematological malignancies. Over the years, approvals were incorporating a more diverse collection of distinct biomarkers, from 3 in 2011 to 16 in 2022. Overall, HER2, hormone receptor (HR), EGFR, ALK, BRAF, and PD-L1-defined populations received the highest numbers of approvals. The FDA decision on approving a biomarker-defined or an all-comers population may depend on a number of factors and may evolve over time based on emerging evidence. The review discusses selected FDA approvals where a pivotal trial enrolled an all-comers population but the approved indication was restricted to a biomarker-defined population, as well as challenges in clinical trial design in the context of precision medicine. The prominent role of biomarkers in optimizing trial design and identifying a population most likely to benefit from treatment underlines the significance of a comprehensive understanding of disease biology and drug mechanisms. Our review illustrates that biomarker-driven approaches enhance the likelihood of identifying optimal patient populations, potentially streamlining trials through accelerated approval pathways for cancer drug development.

Radioligand therapies in meningioma: Evidence and future directions.

Meningiomas are the most common intracranial neoplasms in adults. While most meningiomas are cured by resection, further treatment by radiotherapy may be needed, particularly in WHO grades 2 and 3 tumors which have an increased risk of recurrence, even after conventional therapies. Still, there is an urgent need for novel therapeutic strategies after the exhaustion of local treatment approaches. Radionuclide therapies combine the specificity of tumor-specific antibodies or ligands with the cytotoxic activity of radioactive emitters. Alongside this, integrated molecular imaging allows for a noninvasive assessment of predictive biomarkers as treatment targets. Whereas the concept of "theranostics" has initially evolved in extracranial tumors such as thyroid diseases, neuroendocrine tumors, and prostate cancer, data from retrospective case series and early phase trials underscore the potential of this strategy in meningioma. This review aims to explore the available evidence of radionuclide treatments and ongoing clinical trial initiatives in meningioma. Moreover, we discuss optimal clinical trial design and future perspectives in the field, including compound- and host-specific determinants of the efficacy of "theranostic" treatment approaches.

Optimizing trial design in studies of postorgasmic illness syndrome.

Optimizing trial design in studies of postorgasmic illness syndrome.

Treatment patterns and prognosis in patients with Bacillus Calmette-Guérin-exposed high-risk non-muscle invasive bladder cancer: a real-world data analysis.

The International Bladder Cancer Group designated the subgroup that is resistant to Bacillus Calmette-Guérin (BCG) but does not meet the criteria for BCG-unresponsive NMIBC as "BCG-exposed high-risk NMIBC" to guide optimal trial design. We aimed to investigate the treatment patterns and prognoses of patients with BCG-exposed NMIBC. We conducted a retrospective chart review of 3283 patients who received intravesical BCG therapy for NMIBC at 14 participating institutions between January 2000 and December 2019. Patients meeting the criteria for BCG-exposed and BCG-unresponsive NMIBC, as defined by the Food and Drug Administration and International Bladder Cancer Group, were selected. To compare treatment patterns and outcomes, high-risk recurrence occurring more than 24months after the last dose of BCG was defined as "BCG-treated NMIBC." In addition, we compared prognoses between BCG rechallenge and early cystectomy in patients with BCG-exposed NMIBC. Of 3283 patients, 108 (3.3%), 150 (4.6%), and 391 (11.9%) were classified as having BCG-exposed, unresponsive, and treated NMIBC, respectively. BCG-exposed NMIBC demonstrated intermediate survival curves for intravesical recurrence-free and progression-free survival, falling between those of BCG-unresponsive and treated NMIBC. Among patients with BCG-exposed NMIBC, 48 (44.4%) received BCG rechallenge, which was the most commonly performed treatment, and 19 (17.6%) underwent early cystectomy. No significant differences were observed between BCG rechallenge and early cystectomy in patients with BCG-exposed NMIBC. The newly proposed definition of BCG-exposed NMIBC may serve as a valuable disease subgroup for distinguishing significant gray areas, except in cases of BCG-unresponsive NMIBC.

Computed tomographic angiography measures of coronary plaque in clinical trials: opportunities and considerations to accelerate drug translation.

Atherosclerotic coronary artery disease (CAD) is the causal pathological process driving most major adverse cardiovascular events (MACE) worldwide. The complex development of atherosclerosis manifests as intimal plaque which occurs in the presence or absence of traditional risk factors. There are numerous effective medications for modifying CAD but new pharmacologic therapies require increasingly large and expensive cardiovascular outcome trials to assess their potential impact on MACE and to obtain regulatory approval. For many disease areas, nearly a half of drugs are approved by the U.S. Food & Drug Administration based on beneficial effects on surrogate endpoints. For cardiovascular disease, only low-density lipoprotein cholesterol and blood pressure are approved as surrogates for cardiovascular disease. Valid surrogates of CAD are urgently needed to facilitate robust evaluation of novel, beneficial treatments and inspire investment. Fortunately, advances in non-invasive imaging offer new opportunity for accelerating CAD drug development. Coronary computed tomography angiography (CCTA) is the most advanced candidate, with the ability to measure accurately and reproducibly characterize the underlying causal disease itself. Indeed, favourable changes in plaque burden have been shown to be associated with improved outcomes, and CCTA may have a unique role as an effective surrogate endpoint for therapies that are designed to improve CAD outcomes. CCTA also has the potential to de-risk clinical endpoint-based trials both financially and by enrichment of participants at higher likelihood of MACE. Furthermore, total non-calcified, and high-risk plaque volume, and their change over time, provide a causally linked measure of coronary artery disease which is inextricably linked to MACE, and represents a robust surrogate imaging biomarker with potential to be endorsed by regulatory authorities. Global consensus on specific imaging endpoints and protocols for optimal clinical trial design is essential as we work towards a rigorous, sustainable and staged pathway for new CAD therapies.

Round Table Discussion on Optimal Clinical Trial Design in Precursor Multiple Myeloma.

While the current approach to precursor hematologic conditions is to "watch and wait," this may change with the development of therapies that are safe and extend survival or delay the onset of symptomatic disease. The goal of future therapies in precursor hematologic conditions is to improve survival and prevent or delay the development of symptomatic disease while maximizing safety. Clinical trial considerations in this field include identifying an appropriate at-risk population, safety assessments, dose selection, primary and secondary trial endpoints including surrogate endpoints, control arms, and quality-of-life metrics, all of which may enable more precise benefit-risk assessment.