Oncology Archives

FDA OCE Project Frontrunner: Accelerating First-Line Oncology Drug Development

The U.S. Food and Drug Administration’s Oncology Center of Excellence (OCE) launched Project Frontrunner to shift the paradigm in oncology drug development. Traditionally, novel oncology drugs gain approval for use in patients with later-stage disease and who have exhausted other treatment options. Project Frontrunner challenges this model by encouraging sponsors to pursue initial drug approvals in the earliest feasible disease setting, particularly first-line or treatment-naïve populations.

The conventional late-line strategy for oncology drug development offers fewer regulatory hurdles and facilitates faster enrollment. However, it delays access to potentially life-extending or curative therapies for patients with early-stage disease. Moreover, the biology of tumors in heavily pretreated patients often differs significantly from earlier stages, limiting generalizability. Project Frontrunner seeks to reverse this trend, thereby aligning trial design with patient-centric outcomes.

Here, I discuss the key elements of Project Frontrunner, the statistical complexities of first-line trial design, and the potential impact on sponsors.

Key elements of Project Frontrunner

First-line indication targeting: Encourages drug developers to pursue marketing applications based on trials in treatment-naïve populations, not just refractory or relapsed disease settings.
Regulatory support and early engagement: The FDA offers early scientific engagement with sponsors through Type B and Type C meetings, helping optimize development plans for first-line indications.
Use of randomized controlled trials (RCTs): Promotes the use of RCTs in early-stage disease rather than single-arm studies in late-stage patients, aiming for more robust and generalizable evidence.
Expedited programs compatibility: Supports use of breakthrough therapy designation, priority review, and accelerated approval, even when targeting earlier lines of therapy.

Practical implications for trialists

Trial design complexity: Sponsors must design larger, more rigorous trials, often needing comparator arms, which may increase cost and duration but improve scientific robustness.
Patient recruitment considerations: Recruiting treatment-naïve patients can be more competitive and ethically challenging, requiring careful protocol development and site coordination.
Strategic endpoint selection: Trialists must select endpoints that reflect long-term clinical benefit (e.g., progression-free survival, overall survival), rather than short-term surrogate markers typically used in late-line settings.

Statistical complexities in first-line trial design

Designing oncology trials for first-line indications — as encouraged by Project Frontrunner — brings increased statistical and methodological complexity compared to traditional late-line trials. The rigor demanded by earlier-stage settings requires careful planning to ensure validity, power, and regulatory acceptability.

Randomized comparators and control integrity

Trials typically require active control arms rather than historical controls. Selecting an appropriate standard-of-care comparator and maintaining blinding (where feasible) becomes essential to minimize bias and strengthen inference.

Longer time horizons for endpoints

In first-line disease, progression-free survival (PFS) and overall survival (OS) require longer follow-up, increasing risk of loss to follow-up and requiring more robust methods for censoring and handling missing data.

Multiplicity adjustments and hierarchical testing

With multiple endpoints — such as PFS, OS, objective response rate (ORR), and quality of life — multiplicity becomes a critical issue. Sponsors may need hierarchical testing strategies or gatekeeping procedures to control Type I error.

Interim analysis and adaptive design considerations

Sponsors may wish to incorporate group-sequential designs or adaptive features (e.g., sample size re-estimation), but these add statistical complexity and must be pre-specified with strong rationale to be acceptable to regulators.

Subgroup analyses and biomarker stratification

Treatment-naïve populations may be heterogeneous. Stratification by biomarkers or disease subtype may be necessary, but raises statistical power concerns and increases the risk of false discovery if not pre-specified and adjusted.

Likely impact on sponsors

Project Frontrunner presents both opportunities and challenges for drug developers aiming to target earlier lines of oncology treatment. Below are key advantages and disadvantages for sponsors engaging with this program:

Advantages

Market leadership and differentiation: Gaining approval for a first-line indication can position a therapy as the standard of care, offering strategic advantage over drugs only approved for late-line use.
Extended commercial exclusivity: Earlier approval typically translates into longer duration of market exclusivity, enhancing revenue potential before generics or biosimilars enter the market.
Clinical value and branding: Drugs proven effective in first-line settings may be perceived as more effective and versatile, strengthening the sponsor’s brand and clinical reputation across stakeholders, including physicians and payers.

Disadvantages

Higher development costs and risk: Trials in earlier-stage patients typically require larger sample sizes, randomized designs, and longer follow-up, increasing overall trial costs and investment risk.
Increased regulatory scrutiny: Early-line trials are subject to higher evidentiary standards, with greater emphasis on demonstrating long-term clinical benefit (e.g., overall survival), making approval more difficult.
Competitive recruitment environment: Enrolling treatment-naïve patients is often slower and more competitive, as these patients may have multiple treatment options and may be hesitant to join experimental arms.

Final thoughts

Project Frontrunner represents a bold step by the FDA to reshape oncology drug development. While it demands more rigorous trial designs and greater investment from sponsors, it aligns closely with patient-centric goals: bringing promising therapies to those who need them most, earlier in their disease journey. For sponsors willing to embrace these challenges, the program offers a chance to lead in an increasingly competitive oncology landscape.

James Matcham, VP Strategic Consulting, and Pranav Yajnik, Senior Consultant, will be hosting a Cytel webinar on August 20, 2025, where they will provide an overview of Project Frontrunner and its implications for oncology drug development. They will also explore, using a case study, how innovative trial design strategies can lead to faster, more robust pathways to market for oncology therapies.

Advancing Equity in Health Technology Assessment: Lessons from CAR T-Cell Therapies

Chimeric antigen receptor (CAR) T-cell therapies, classified as advanced therapy medicine products, have revolutionized the treatment landscape for certain hematological cancers, providing new hope to patients who previously had limited options. Since the U.S. FDA approved tisagenlecleucel (Kymriah) and axicabtagene ciloleucel (Yescarta) in 2017 for relapsed or refractory B-cell precursor acute lymphoblastic leukemia and large B-cell lymphoma, respectively, evidence has suggested that CAR T-cell therapies could offer a potentially curative approach in a range of other hematological conditions.1,2,3,4

However, despite their potential to improve patient outcomes, access to CAR T-cell therapies remains inconsistent due to cost, delivery complexity, and manufacturing challenges. Additionally, disparities in access related to social determinants of health (SDOH) further limit equitable benefits, disproportionately impacting marginalized populations (such as those living in rural areas, individuals with no family or social networks, and older people).

Health technology assessment (HTA) has traditionally focused on clinical outcomes and cost-effectiveness. Although health equity has been recognized as a distinct value element in HTA, and relevant frameworks and guidelines exist, it is not routinely integrated into decision-making. As such, CAR T-cell therapies represent a valuable case study for better understanding and advancing equity considerations in HTA.

What are CAR T-cell therapies?

CAR T-cell therapies are a type of immunotherapy that modify a patient’s T-cells to target and attack cancer cells, offering effective options for relapsed or refractory hematological cancers. This process involves extracting, modifying, and reinfusing the cells, followed by close monitoring for severe adverse events. Beyond their current approved indications, CAR T-cell therapies are also being investigated for several other hematological malignancies, as well as in solid tumors and non-cancer indications such as autoimmune conditions.4

Delivering CAR T-cell therapies presents significant challenges for healthcare systems due to their complexity, high cost, and the need for specialized infrastructure and expertise. The treatment requires apheresis, cell manufacturing, conditioning therapy, and intensive post-infusion monitoring, all conducted at accredited centers, often located in major urban areas.5 Successful delivery also requires coordination among a multidisciplinary team of physicians, nurses, and pharmacists, along with investment in treatment center infrastructure, including intensive care unit capacity and specialized training to manage severe adverse events (e.g., cytokine release syndrome and neurotoxicity).5

CAR T-cell therapies: Highlighting equity concerns in access to innovative treatments

Ensuring that equitable access to healthcare is considered in the HTA decision-making, particularly for high-cost, innovative treatments like CAR T-cell therapy, has become a growing concern. Despite advancements in science, therapeutic applications, and complication management, access to CAR T-cell therapy remains limited, with only a small percentage of eligible patients receiving treatment.6,7 This restricted access stems from challenges specific to CAR T-cell therapy, such as high costs, complex logistics, and manufacturing constraints, which are compounded by factors related to SDOH and equity.

Equity gaps are evident in disease incidence and prevalence, treatment patterns, and outcomes of patients eligible for CAR T-cell therapies. For example, racial and ethnic minorities, particularly Black and Hispanic populations, experience higher rates of certain hematological malignancies, yet are underrepresented in clinical trials that inform CAR T-cell therapy approvals.8,9 This leads to gaps in effectiveness and safety data across populations. Furthermore, differences in diagnosis and referral patterns contribute to inequities, with marginalized groups less likely to be referred to specialized centers due to limited provider awareness or implicit biases. Older adults, who could benefit from CAR T-cell therapies, are often excluded from trials, limiting evidence for their use in this population.10 SDOH, such as geographic remoteness and socioeconomic status, exacerbate inequities in access to CAR T-cell therapies once they are approved. Patients living in rural areas face logistical and financial barriers to reaching treatment centers, while individuals from lower socioeconomic backgrounds struggle with transportation, caregiving responsibilities, and lost wages.11,12 These overlapping disparities create a cumulative burden, limiting equitable access and worsening outcomes for historically underserved groups.

Exploring equity factors in HTAs of CAR T-cell therapies and the journey toward inclusive access

Traditional HTA frameworks have historically overlooked equity considerations, prioritizing clinical efficacy and cost-effectiveness while neglecting how SDOH and equity factors affect patient access and outcomes. This gap not only exacerbates disparities but also fails to incentivize health technology developers to commit to systematic evidence gathering and addressing these issues in their evidence submissions. While several modified economic modeling approaches that account for equity considerations exist (e.g., distributional cost-effectiveness analyses, equity-based weighting, multi-criteria decision analysis), there is a lack of consensus on which approach is best and how these methods can systematically be incorporated into HTA.13,14 As a result, HTAs often do not account for the unique burdens faced by underserved populations, such as indirect costs related to travel, caregiving, and lost income, further exacerbating existing inequities.

Recent commitments to equity from HTA bodies present valuable opportunities to ensure fair access to novel, high-cost therapies.15,16 CAR T-cell therapies, with their complex delivery and high cost, serve as a compelling case study for examining how HTA bodies incorporate equity considerations into their assessments. To explore this further, we conducted a review of 18 HTAs from Canada’s Drug Agency and the National Institute for Health and Care Excellence, focusing on six CAR T-cell therapies. Our review found that most submissions acknowledged disparities in disease incidence, treatment, and outcomes based on race, socioeconomic status, diagnosis and referral patterns, and age. These disparities were often linked to financial and geographical barriers that disproportionately affect marginalized groups. However, there were limited and inconsistent efforts to quantify these factors in the economic modeling or in the analysis of the clinical evidence submitted. This likely reflects the fact that HTA bodies do not routinely require sponsors to quantify equity concerns within their submissions, leading both decision-makers and companies to potentially overlook these issues.

Cytel will present the results of this review at the 2025 ISPOR conference in Montreal, Canada, where we will explore how gaps in HTA evaluations can inadvertently perpetuate inequities in access to CAR T-cell therapies. Join us at our podium session to learn more about how incorporating equity considerations into HTA processes can promote more equitable outcomes and ensure that all patients, regardless of their background, can benefit from CAR T-cell therapies. Do not miss this opportunity to engage in the discussion on advancing inclusive access to high-cost, innovative therapies.

Addressing equity concerns in CAR T-cell therapies: Strategies for inclusive access

Cytel can support pharmaceutical clients in addressing equity concerns through the following offerings:

Innovative trial designs that consider elements of health equity
Generation of real-world evidence to supplement trial programs
Lifecycle evidence generation to support value in diverse groups of patients
Advanced analytics, such as transportability analyses, to maximize the use of evidence generated in other settings
Quantifying the impact of inequalities in the value proposition of new health technologies.

Oncology Drug Development Under Project Optimus: Case Studies

Conducting a successful oncology development program under Project Optimus requires increased emphasis on determining the optimal dose for the compound under study. Rather than a singular focus on the maximum tolerated dose (MTD), oncology drug development under Project Optimus requires one to develop an approach based on all available data. This includes safety, response rate, biomarker responses, and pharmacokinetics.

The increased emphasis on determining the optimal dose has led to several changes in how clinical trials for oncology drugs are conducted. Here, we will describe several case studies that will demonstrate how innovative study designs and clinical pharmacology may be used to speed development of oncology assets under Project Optimus.

Dose escalation in oncology drug development

There are three main goals of dose escalation in oncology drug development: to determine 1) the dose range where efficacy might be safely explored; 2) the maximum tolerated dose, if obtainable; and 3) the minimum active dose.

A wide range of designs for dose escalation can be used, the majority of which fall into one of two categories: algorithm-based and model-based designs. These design categories differ in several ways, as illustrated in Figure 1.

Figure 1: Comparison of algorithm and model-based methods

Algorithm-based methods are conventional design methods that use prespecified rules to determine dose escalation and de-escalation. The classic 3+3 design, for example, is still used fairly frequently, despite its documented shortcomings. For example, the “3+3” design may recommend a Phase 2 dose that is too high, it is unable to include intermediate doses, and there are difficulties with handling cohort numbers that are not multiples of three.

Model-based methods, on the other hand, have significant advantages over algorithm-based methods, since prior information may be used. These adaptive design methods may provide information on intermediate doses not studied. However, because the “3+3” design has been in use for so long, there is considerable inertia among trialists to adopt better designs.

Newer algorithmic designs, such as mTPI-2, BOIN, i3+3, and model-based designs, such as BLRM, should be carefully considered.

In the age of targeted immune-oncology agents, the concept of the Maximum Tolerated Dose (MTD) is assuming less importance, as the optimally efficacious dose of these products is usually lower than the MTD. There are newer study designs that consider not only toxicity, but also efficacy. These designs, such as J3+3, PRINTE, TEPE, EFFTOX and UBOIN, are more suited for modern targeted agents.

Pharmacokinetics and pharmacodynamics in oncology drug development: Case studies

There are many reasons to closely monitor pharmacokinetics (PK) during the initial dose escalation phase, including confirmation of exposure predictions, sufficient bioavailability (for oral or subcutaneous drugs), and the potential need for changes in the infusion rate, sampling scheme, or dosing regimen.

To illustrate this: in one case we encountered, the observed exposure was considerably different than what was predicted, and so considerable re-work of doses and dosing regimens had to be performed. The good news is that this was done early, thus minimizing the number of patients exposed to sub-optimal doses.

In another example, poor oral bioavailability was observed early in a dose escalation trial, thus allowing the trial to close early, again minimizing the number of patients treated with a sub-optimal dosing regimen. Building PK models of your drug early allows quick evaluation of the impact of different regimens and infusion times.

Dosing of oncology agents based on some measure of body size has a long history in oncology. Most immune-oncology agents are dosed based on body weight, as clearance of monoclonal antibodies (mAbs) is proportional to weight. Therefore, weight-based dosing is often used in the initial Phase 1 trial. In later studies, weight-based dosing may result in increased costs, as patient kits will have to contain extra vials of the drug to account for the wide range of patient weights that may be encountered. This can result in considerable waste if these extra vials are not used. Post-approval, weight-based dosing can also result in waste, as more than one vial of the drug may have to be used for larger patients, with the remaining portion in the second vial being discarded. To transition from weight-based to fixed dosing, one should perform simulations of weight-based and fixed doses and choose the fixed dose that most closely matches the weight-based exposure (AUC or Cmax).

The role and advantages of measuring pharmacodynamic biomarkers in oncology is sometimes not clear. There are no established surrogate endpoints in oncology, so what do these markers add to your program? The answer is evidence of target engagement. A biomarker closely linked to the mechanism of action of the drug, which changes in response to various doses of the compound, gives added “reason to believe” in the new drug’s mechanism of action.

Exposure-response analyses also help evaluate the relationship between exposure and safety. Evidence of maximum target engagement, coupled with safety and efficacy data, adds credence to the overall data set. (For two case studies illustrating the usefulness of exposure-response data in helping to interpret the overall safety and efficacy data, watch the webinar linked below). One case we encountered showed how exposure-response data for both a safety endpoint and a target engagement endpoint helped with the interpretation of efficacy data, which was promising, but difficult to interpret. The use of biomarkers in an exposure-response context allowed the selection of doses for Phase 2. Another case study showed that, based on a clear exposure-response relationship with a safety endpoint, it would be advantageous to study additional patients at lower doses in order to find a lower dose with the same level of efficacy, but a lower level of toxicity.

Final takeaways

The goal of any study under Project Optimus is to determine the optimal dose, not necessarily the MTD, which is of lesser importance. Newer study design options for immune-oncology products developed under Project Optimus should be considered. These designs have significant advantages over the classical “3+3” design. The pharmacokinetics of your compound should be quantified as early as possible in development in order to 1) confirm exposure, 2) confirm sufficient bioavailability (if administered orally), and 3) investigate whether fixed-dosing (rather than weight-based dosing) may be used. Performing exposure-response analyses using reliable biomarkers can aid in decision-making on doses, by showing target engagement. Exposure-response analyses using safety endpoints can also be extremely helpful in determining doses to take further into development.

Interested in learning more? Our recent webinar, “Oncology Drug Development Under Project Optimus: Case Studies” gives a full breakdown of the case studies mentioned here, and more. Watch on demand:

Adaptive Population Enrichment Designs in Oncology Trials

Enrichment strategies in oncology clinical trials have become increasingly important in the era of precision medicine. These strategies involve selecting patients with specific pre-treatment characteristics that may make them more likely to respond to a targeted therapy, thereby increasing the efficiency and effectiveness of the clinical trial.

In oncology, enrichment often involves selecting patients based on specific biomarkers or genetic mutations that are associated with the drug’s mechanism of action. For example, trials for drugs targeting HER2/neu in breast cancer or EGFR mutations in lung cancer often use enrichment strategies to include only patients whose tumors express these markers. This approach not only increases the likelihood of detecting efficacy but also helps identify the patient population most likely to benefit from the treatment.

FDA guidance on enrichment strategies for clinical trials

The FDA has issued guidance on enrichment strategies for clinical trials. This guidance defines enrichment as the prospective use of patient characteristics to select a study population more likely to demonstrate a drug effect. The guidance outlines three main categories of enrichment strategies:

Strategies to decrease heterogeneity, which aim to reduce variability and increase study power.
Prognostic enrichment strategies, which select patients with a higher likelihood of having a disease-related endpoint event or substantial worsening of condition.
Predictive enrichment strategies, which select patients more likely to respond to the drug based on physiological, disease characteristics, or previous response to similar drugs.

The FDA encourages the use of these strategies to enhance the understanding of the benefit-risk relationship in both the overall and the enriched population. They also emphasize the importance of properly describing study findings in drug labeling.

Benefits and trade-offs of adaptive population enrichment designs

Adaptive population enrichment designs offer sponsors additional flexibility, allowing for adjustment of eligibility criteria based on accumulating data during the trial, potentially leading to more efficient drug development and better-targeted therapies for cancer patients.

These designs start by enrolling a broad patient population but have the flexibility to restrict future recruitment after an interim analysis to patient subgroups showing greater treatment benefit. Trials designed in accordance with these principles simultaneously evaluate treatment effects in both the overall population and specific subpopulations of interest, while maintaining statistical power. By allowing for data-driven adjustments to the study population, adaptive population enrichment designs can increase trial efficiency, direct resources toward promising subgroups, and improve the likelihood of identifying effective treatments for specific patient sub-populations.

However, adaptive population enrichment designs also present several statistical challenges that require careful planning and consideration. One of the primary issues is controlling the Type I error rate, as these designs involve interim unblinded analyses and potential changes to the study population. This necessitates the use of specialized statistical methods to ensure the validity of the trial results.

Sample size determination is another critical aspect that demands thorough planning. Sponsors must consider various scenarios, including different treatment effects in subpopulations and potential adaptation decisions, to ensure adequate statistical power for detecting treatment effects in both the overall population and selected subgroups. The pre-specification of adaptation rules, hypothesis tests, and statistical methods for combining data from different stages of the trial is also essential for maintaining the integrity of the study.

Finally, there are also trade-offs and considerations regarding the timing of the interim analyses, the underlying prevalence of the sub-populations, the magnitude of the differential effects.

Final takeaways

Enrichment strategies can increase the efficiency and effectiveness of oncology trials by selecting patients more likely to respond to a targeted therapy. However, while adaptive population enrichment designs allow for adjustments based on interim data, their complexity introduces statistical challenges that require careful planning. Despite these challenges, the ability to direct resources toward subgroups showing promise holds significant potential for accelerating the development of cancer therapies.

Interested in learning more? Register today for our webinar “Oncology Clinical Trials: Design Considerations in Adaptive Population Enrichment Trials” on October 9, 2024.

The webinar will provide a comprehensive overview of statistical aspects of adaptive enrichment trials, regulatory requirements for pre-specification of design elements, and benefits and trade-offs, as well as insights from past engagements with sponsors and regulatory agencies.

Navigating Dose Optimization in Drug Development: Answering Questions about Project Optimus

In the complex world of drug development, dose optimization plays a crucial role. Project Optimus, a groundbreaking initiative, focuses on refining this process for the development of oncology drugs.

During our recent webinar, we received dozens of questions about this new paradigm in oncology drug development. This blog post highlights some of those questions and answers from our expert speakers.

How critical is the biomarker-clinical efficacy relationship in choosing the minimum effective dose?

While the biomarker-clinical efficacy relationship is vital, it’s important to acknowledge that most biomarkers do not act as surrogate endpoints, meaning they don’t necessarily predict clinical efficacy. Instead, a more pragmatic approach is to treat biomarkers as indicators of target engagement. By doing this, we can use the biomarker response as support for the clinical response observed in the trial. Ideally, the biomarkers measured in Phase 1 should measure this target engagement as directly as possible.
Additionally, pharmacodynamic (PD) endpoints are very useful (in conjunction with clinical response data) in defining the dose range to be studied in Phase 2. Typically, two doses are chosen — one that maximizes the biomarker response and a higher dose to address any uncertainties about the drug’s mechanism. Although this strategy isn’t flawless, it is currently one of the best approaches available.

What are your recommendations for measuring and analyzing biomarkers to select an appropriate dose? What sampling timings are recommended?

The ideal biomarkers directly measure target engagement and should have some correlation with a positive response in animal models. Choosing appropriate biomarkers is challenging and should involve collaboration among clinical pharmacology, translational medicine, and clinical teams.

Sampling timing is crucial. Many biomarkers lose their utility due to insufficient sampling. For instance, measuring biomarkers only after the first dose can miss long-term changes. Early involvement of clinical pharmacology or pharmacometrics teams can ensure that biomarkers and sampling schemes are optimally selected.

How should investigational therapies with a myriad of biomarkers still being studied in Phase 1a/b be evaluated?

This situation is complex and depends a great deal on the target of the investigational therapy. Ideally, biomarkers should directly measure target engagement and be chosen thoughtfully, involving input from pharmacometrics, translational medicine, and clinical teams. It’s better to focus on one or two well-selected biomarkers rather than several chosen without careful consideration.

Could you explain the concept of “short half-life”?

The term “short half-life” is relative to the dosing interval. For instance, a drug with a 30-day half-life would not be considered short in most cases. However, if the drug is dosed annually, a 30-day half-life would be relatively short, suggesting no accumulation of the drug.

A more concrete definition of “short” could be a half-life that results in no accumulation for a specific dosing regimen. For example, a drug with a 4-hour half-life administered weekly would generally not accumulate, and pre-dose values would be zero. In such cases, collecting pre-dose samples would be redundant and post-dose samples would be more informative.

What are the advantages of conducting studies in healthy subjects for small molecule targeted therapies versus starting directly in patients?

Conducting small studies in healthy volunteers (HVs) can provide critical information on the drug’s exposure and half-life in humans, aiding in the design of patient studies. Additionally, safety profiles can be more clearly distinguished in HVs.

However, investors might prefer patient data over HV data. A compromise could be a two-part study, beginning with HVs and followed by patients, allowing for initial pharmacokinetics (PK) knowledge while ultimately providing patient data, which is more compelling to stakeholders.

Most often though, early oncology studies are conducted in patients, so as not to expose volunteers with healthy body systems to potentially toxic treatments.

What does minimal overlap in PK exposure translate into, especially in accelerated titrated designs when there might be insufficient information on PK variability?

The key factor here is the inter-patient variability in clearance. If there is uncertainty about the variability estimate, simulating various estimates can help determine the minimal overlap. For example, if variability ranges between 25% and 45%, simulations using these estimates can guide dose selection to minimize overlap, considering safety and other factors.

By addressing these questions, Project Optimus aims to refine dose optimization strategies, ultimately improving the effectiveness and safety of new therapies.

Interested to learn more? Join our office hours on Project Optimus with James Matcham and Michael Fossler on September 18 — sign up today!

Simulating Survival Outcomes for Unanchored Simulated Treatment Comparisons: Guidance on Censoring Approaches

Unanchored simulated treatment comparisons (STCs) are a valuable tool for manufacturers navigating the health technology assessment (HTA) landscape. When head-to-head clinical trials are unavailable, STCs allow for population-adjusted indirect comparisons between a single-arm trial and an external control arm.

Using regression modeling to predict outcomes based on patient characteristics, STCs enable comparisons in the absence of a common comparator. This is particularly valuable when evaluating novel therapies, especially in rare or specialized disease areas where randomized controlled trials may be limited.

Understanding the Critical Role of DMCs in Oncology Studies

In clinical research, particularly within oncology, Data Monitoring Committees (DMCs) play a pivotal role in ensuring the integrity and safety of clinical trials. With the high volume of oncology studies and the extensive use of DMCs in these trials, it is essential to understand the specific nuances and challenges these committees face. Here, I provide an overview of the critical aspects of DMCs in oncology studies.

Oncology Clinical Trials: Design Trends in Biomarker Research

Oncology research has seen many changes and advances in recent decades, from new therapies in combination with backbone chemotherapy to novel treatments targeting malignancies, and compounds targeting specific disease biomarkers at the genetic mutation level. The latter approach has called to question large, relatively long clinical studies assessing the safety and efficacy of treatments against a large population defined at the tumor level. Rather, research at the subpopulation or biomarker level has garnered much more interest as targeted treatments are being developed.

This focus on subpopulations and biomarkers is changing how researchers approach clinical trials in oncology and helps resolve several issues with larger clinical trials. For example, treatment effects may be diluted in a heterogeneous population, possibly resulting in an underpowered study. Furthermore, a large trial in a heterogeneous population may place patients for whom the drug is ineffective at risk of serious adverse events. On the other hand, restricting enrollment to a target subgroup without sufficient evidence may deny a large segment of the patient population access to a potentially beneficial treatment. This blog post will briefly introduce two statistical approaches addressing the rise of more specific study populations: predefined subpopulation statistical analysis in the context of a larger trial population and population enrichment of the more promising subgroup within an ongoing study.

Subpopulation Analysis

Subpopulation testing and analysis is a phase III clinical trial design strategy in which a subset of the study population is selected based on patient characteristics that may be more likely to respond to the treatment under investigation. Identifying and analyzing specific subpopulations allows the researcher to explore whether a treatment leads to different effects in a pre-designated subpopulation. A subpopulation can be defined by any stratification characteristic such as gender or geography, and in oncology clinical trials, specific biomarkers identified within a study population.

This type of approach to clinical research has several significant benefits in Oncology studies:

A large trial in a heterogeneous population may place patients for whom the drug is ineffective at risk of serious adverse events.

In a heterogenous population, the treatment effect may be diluted, possibly resulting in an underpowered study.

Restricting enrollment to the targeted subgroup without sufficient statistical evidence of lack of efficacy in the non‐targeted subgroup may eliminate beneficial treatment options for patients.

Subpopulation analysis allows for treatment recommendations based on individual characteristics.

As with any novel adaptive design approach, subpopulation analysis requires several considerations at the design stage. These considerations include the specific definition of the subpopulations for analysis in the study, the appropriate timing for an interim analysis, the methods used for hypothesis testing and type-1 error preservation, and the sequence of hypothesis testing of the different subpopulations and/or the full study population.

With these considerations in mind, rigorous planning and testing in the design stage of such a clinical trial is critical. Cytel’s East Horizon adaptive clinical trial design software offers a unique solution for the planning and testing of a clinical trial design that includes subpopulation analysis. In Cytel’s solution, hypothesis testing for the full and subpopulations can be performed using graphical multiple comparison procedures (gMPC) with a weighted Bonferroni procedure employed for closed testing. This method of hypothesis testing uses directed, weighted graphs where each node corresponds to a single hypothesis. A transition matrix is used as a complement to specify the weights and generate an intuitive diagram. Finally, a simple algorithm sequentially tests the individual hypotheses using the specified weights and hierarchies.

Population Enrichment

Population Enrichment is an adaptive clinical trial approach that includes the prospective use of any patient characteristic to obtain a study population in which detection is more likely than in the unselected population. There are two types of population enrichment: Prognostic Enrichment, in which a high-risk patient population is identified based on a biomarker, and Predictive Enrichment, in which the researchers identify a patient group more likely to respond to treatment. Some industry trends that have contributed to the popularization of this adaptive design method include the soaring costs of clinical trial execution, a move away from a “one-size-fits-all” approach to clinical development, and the rising interest in individualized medicine. This adaptive design approach has several benefits, including the identification of highly responsive patient populations, the efficient detection of a treatment effect in a smaller sample size, and the ability to identify beneficial treatments for a subgroup of patients that may have failed with a broader population in a more traditional study design.

Population enrichment can be seen as an extension of the sample size re-estimation (SSR) methodology, which we discussed in more depth in a previous blog post.

In the enrichment adaptive approach, a pre-specified number of subjects comprising the entire population, designated as cohort 1, is tested in an interim analysis, and a data monitoring committee reviews the results to assess efficacy or futility against predetermined thresholds. Suppose the analysis shows promising results for only a specific subpopulation of interest in the study, this population is “enriched” with additional patient enrollment in the remaining number of subjects of the study, designated as cohort 2, to enhance data collection for only this subgroup of interest and increase the overall probability of success of the study. As with any adaptive approach, this method has specific considerations, including closed testing with a p-value combination, the preservation of type-1 errors, and additional special considerations requiring attention in event-driven trials like most oncology ones.

Final Takeaways

Both subpopulation analysis and population enrichment are adaptive approaches to modern trial designs in oncology that offer great hope for researchers and patients alike. As the focus on specific patient populations narrows, these adaptive design types are gaining industry traction. Software-guided clinical trial design and simulation using tools such as East Horizon ensure adaptive elements are incorporated thoughtfully and are rigorously tested prior to trial launch.

Learn more about these approaches in our upcoming webinar ‘’Oncology Clinical Trials: Design Trends in Biomarker-Driven Research’’ with Boaz Adler and Valeria Mazzanti.

The Role of External Data in Oncology Drug Development

Randomized controlled trials (RCTs) remain the gold standard for the evaluation of the safety and effectiveness of a new treatment. However, in a number of cases alternative approaches leveraging external data (i.e., data from outside of a clinical trial) — ranging from single arm trials to augmented RCTs — can be appropriate. Here, we discuss how to leverage and incorporate external data in drug development, focusing on the use of external control arms and Bayesian borrowing.

Advancing Oncology Trials with Bayesian Basket Designs

Written by Yuan Ji, Professor of Biostatistics at The University of Chicago and Mansha Sachdev, Senior Marketing Manager, Content

The need for innovative and efficient trial designs has become increasingly apparent in the evolving landscape of oncology drug development. Traditional clinical trials often focus on a single cancer type, requiring multiple individual trials to assess a treatment’s efficacy across different cancer subtypes. This approach can be both resource-intensive and time-consuming. Basket trials, however, offer an innovative solution by allowing simultaneous evaluation of a single therapy across multiple cancer types or subtypes that share common molecular characteristics. This method promises to enhance precision and efficiency in oncology drug development, particularly when combined with Bayesian statistical methods.

Here, we outline the potential transformative role of Bayesian basket trials in oncology drug development.

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Key elements of Project Frontrunner

Practical implications for trialists

Statistical complexities in first-line trial design

Randomized comparators and control integrity

Longer time horizons for endpoints

Multiplicity adjustments and hierarchical testing

Interim analysis and adaptive design considerations

Subgroup analyses and biomarker stratification

Likely impact on sponsors

Advantages

Disadvantages

Final thoughts

What are CAR T-cell therapies?

CAR T-cell therapies: Highlighting equity concerns in access to innovative treatments

Exploring equity factors in HTAs of CAR T-cell therapies and the journey toward inclusive access

Addressing equity concerns in CAR T-cell therapies: Strategies for inclusive access

Dose escalation in oncology drug development

Figure 1: Comparison of algorithm and model-based methods

Pharmacokinetics and pharmacodynamics in oncology drug development: Case studies

Final takeaways

Interested in learning more? Our recent webinar, “Oncology Drug Development Under Project Optimus: Case Studies” gives a full breakdown of the case studies mentioned here, and more. Watch on demand:

FDA guidance on enrichment strategies for clinical trials

Benefits and trade-offs of adaptive population enrichment designs

Final takeaways

Interested in learning more? Register today for our webinar “Oncology Clinical Trials: Design Considerations in Adaptive Population Enrichment Trials” on October 9, 2024.

How critical is the biomarker-clinical efficacy relationship in choosing the minimum effective dose?

What are your recommendations for measuring and analyzing biomarkers to select an appropriate dose? What sampling timings are recommended?

How should investigational therapies with a myriad of biomarkers still being studied in Phase 1a/b be evaluated?

Could you explain the concept of “short half-life”?

What are the advantages of conducting studies in healthy subjects for small molecule targeted therapies versus starting directly in patients?

What does minimal overlap in PK exposure translate into, especially in accelerated titrated designs when there might be insufficient information on PK variability?

Subpopulation Analysis

Population Enrichment

Final Takeaways