Biomarker-Guided Adaptive Trial Designs in Phase II and Phase III: A Methodological Review

Hypoxia-activated prodrugs and (lack of) clinical progress: The need for hypoxia-based biomarker patient selection in phase III clinical trials

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Hypoxia-activated prodrugs have yielded promising results up to phase II trials.

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Implementation of hypoxia-activated prodrugs in the clinic has not been successful.

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Phase III clinical trials lack patient stratification based on tumor hypoxia status.

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Stratification will decrease the number of patients needed and increase success.

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Improvements in hypoxia-activated prodrug design can also increase success rates.

Hypoxia-activated prodrugs (HAPs) are designed to specifically target the hypoxic cells of tumors, which are an important cause of treatment resistance to conventional therapies. Despite promising preclinical and clinical phase I and II results, the most important of which are described in this review, the implementation of hypoxia-activated prodrugs in the clinic has, so far, not been successful. The lack of stratification of patients based on tumor hypoxia status, which can vary widely, is sufficient to account for the failure of phase III trials. To fully exploit the potential of hypoxia-activated prodrugs, hypoxia stratification of patients is needed. Here, we propose a biomarker-stratified enriched Phase III study design in which only biomarker-positive (i.e. hypoxia-positive) patients are randomized between standard treatment and the combination of standard treatment with a hypoxia-activated prodrug. This implies the necessity of a Phase II study in which the biomarker or a combination of biomarkers will be evaluated. The total number of patients needed for both clinical studies will be far lower than in currently used randomize-all designs. In addition, we elaborate on the improvements in HAP design that are feasible to increase the treatment success rates.

Subgroup identification in clinical trials via the predicted individual treatment effect

Identifying subgroups of treatment responders through the different phases of clinical trials has the potential to increase success in drug development. Recent developments in subgroup analysis consider subgroups that are defined in terms of the predicted individual treatment effect, i.e. the difference between the predicted outcome under treatment and the predicted outcome under control for each individual, which in turn may depend on multiple biomarkers. In this work, we study the properties of different modelling strategies to estimate the predicted individual treatment effect. We explore linear models and compare different estimation methods, such as maximum likelihood and the Lasso with and without randomized response. For the latter, we implement confidence intervals based on the selective inference framework to account for the model selection stage. We illustrate the methods in a dataset of a treatment for Alzheimer disease (normal response) and in a dataset of a treatment for prostate cancer (survival outcome). We also evaluate via simulations the performance of using the predicted individual treatment effect to identify subgroups where a novel treatment leads to better outcomes compared to a control treatment.

Point estimation following two-stage adaptive threshold enrichment clinical trials

Recently, several study designs incorporating treatment effect assessment in biomarker-based subpopulations have been proposed. Most statistical methodologies for such designs focus on the control of type I error rate and power. In this paper, we have developed point estimators for clinical trials that use the two-stage adaptive enrichment threshold design. The design consists of two stages, where in stage 1, patients are recruited in the full population. Stage 1 outcome data are then used to perform interim analysis to decide whether the trial continues to stage 2 with the full population or a subpopulation. The subpopulation is defined based on one of the candidate threshold values of a numerical predictive biomarker. To estimate treatment effect in the selected subpopulation, we have derived unbiased estimators, shrinkage estimators, and estimators that estimate bias and subtract it from the naive estimate. We have recommended one of the unbiased estimators. However, since none of the estimators dominated in all simulation scenarios based on both bias and mean squared error, an alternative strategy would be to use a hybrid estimator where the estimator used depends on the subpopulation selected. This would require a simulation study of plausible scenarios before the trial.

Clinical trial design for progressive MS trials

The design of clinical trials is a key aspect to maximizing the possibility to detect a treatment effect. This fact is particularly challenging in progressive multiple sclerosis (PMS) studies due to the uncertainty about the right target and/or outcome in phase-2 studies. The aim of this review is to evaluate the current challenges facing the design of clinical trials for PMS. The selection of patients, the instrumental and clinical outcomes that can be used in PMS trials, and issues in their design will be covered in this report.

Critical concepts in adaptive clinical trials

Adaptive clinical trials are an innovative trial design aimed at reducing resources, decreasing time to completion and number of patients exposed to inferior interventions, and improving the likelihood of detecting treatment effects. The last decade has seen an increasing use of adaptive designs, particularly in drug development. They frequently differ importantly from conventional clinical trials as they allow modifications to key trial design components during the trial, as data is being collected, using preplanned decision rules. Adaptive designs have increased likelihood of complexity and also potential bias, so it is important to understand the common types of adaptive designs. Many clinicians and investigators may be unfamiliar with the design considerations for adaptive designs. Given their complexities, adaptive trials require an understanding of design features and sources of bias. Herein, we introduce some common adaptive design elements and biases and specifically address response adaptive randomization, sample size reassessment, Bayesian methods for adaptive trials, seamless trials, and adaptive enrichment using real examples.

Adaptive designs in clinical trials: why use them, and how to run and report them

Adaptive designs can make clinical trials more flexible by utilising results accumulating in the trial to modify the trial's course in accordance with pre-specified rules. Trials with an adaptive design are often more efficient, informative and ethical than trials with a traditional fixed design since they often make better use of resources such as time and money, and might require fewer participants. Adaptive designs can be applied across all phases of clinical research, from early-phase dose escalation to confirmatory trials. The pace of the uptake of adaptive designs in clinical research, however, has remained well behind that of the statistical literature introducing new methods and highlighting their potential advantages. We speculate that one factor contributing to this is that the full range of adaptations available to trial designs, as well as their goals, advantages and limitations, remains unfamiliar to many parts of the clinical community. Additionally, the term adaptive design has been misleadingly used as an all-encompassing label to refer to certain methods that could be deemed controversial or that have been inadequately implemented.We believe that even if the planning and analysis of a trial is undertaken by an expert statistician, it is essential that the investigators understand the implications of using an adaptive design, for example, what the practical challenges are, what can (and cannot) be inferred from the results of such a trial, and how to report and communicate the results. This tutorial paper provides guidance on key aspects of adaptive designs that are relevant to clinical triallists. We explain the basic rationale behind adaptive designs, clarify ambiguous terminology and summarise the utility and pitfalls of adaptive designs. We discuss practical aspects around funding, ethical approval, treatment supply and communication with stakeholders and trial participants. Our focus, however, is on the interpretation and reporting of results from adaptive design trials, which we consider vital for anyone involved in medical research. We emphasise the general principles of transparency and reproducibility and suggest how best to put them into practice.

Adaptive design clinical trials: a review of the literature and ClinicalTrials.gov

OBJECTIVES

This review investigates characteristics of implemented adaptive design clinical trials and provides examples of regulatory experience with such trials.

DESIGN

Review of adaptive design clinical trials in EMBASE, PubMed, Cochrane Registry of Controlled Clinical Trials, Web of Science and ClinicalTrials.gov. Phase I and seamless Phase I/II trials were excluded. Variables extracted from trials included basic study characteristics, adaptive design features, size and use of independent data monitoring committees (DMCs) and blinded interim analyses. We also examined use of the adaptive trials in new drug submissions to the Food and Drug Administration (FDA) and European Medicines Agency (EMA) and recorded regulators' experiences with adaptive designs.

RESULTS

142 studies met inclusion criteria. There has been a recent growth in publicly reported use of adaptive designs among researchers around the world. The most frequently appearing types of adaptations were seamless Phase II/III (57%), group sequential (21%), biomarker adaptive (20%), and adaptive dose-finding designs (16%). About one-third (32%) of trials reported an independent DMC, while 6% reported blinded interim analysis. We found that 9% of adaptive trials were used for FDA product approval consideration, and 12% were used for EMA product approval consideration. International regulators had mixed experiences with adaptive trials. Many product applications with adaptive trials had extensive correspondence between drug sponsors and regulators regarding the adaptive designs, in some cases with regulators requiring revisions or alterations to research designs.

CONCLUSIONS

Wider use of adaptive designs will necessitate new drug application sponsors to engage with regulatory scientists during planning and conduct of the trials. Investigators need to more consistently report protections intended to preserve confidentiality and minimise potential operational bias during interim analysis.

Optimized adaptive enrichment designs

Based on a Bayesian decision theoretic approach, we optimize frequentist single- and adaptive two-stage trial designs for the development of targeted therapies, where in addition to an overall population, a pre-defined subgroup is investigated. In such settings, the losses and gains of decisions can be quantified by utility functions that account for the preferences of different stakeholders. In particular, we optimize expected utilities from the perspectives both of a commercial sponsor, maximizing the net present value, and also of the society, maximizing cost-adjusted expected health benefits of a new treatment for a specific population. We consider single-stage and adaptive two-stage designs with partial enrichment, where the proportion of patients recruited from the subgroup is a design parameter. For the adaptive designs, we use a dynamic programming approach to derive optimal adaptation rules. The proposed designs are compared to trials which are non-enriched (i.e. the proportion of patients in the subgroup corresponds to the prevalence in the underlying population). We show that partial enrichment designs can substantially improve the expected utilities. Furthermore, adaptive partial enrichment designs are more robust than single-stage designs and retain high expected utilities even if the expected utilities are evaluated under a different prior than the one used in the optimization. In addition, we find that trials optimized for the sponsor utility function have smaller sample sizes compared to trials optimized under the societal view and may include the overall population (with patients from the complement of the subgroup) even if there is substantial evidence that the therapy is only effective in the subgroup.

Fixed and Adaptive Parallel Subgroup-Specific Design for Survival Outcomes: Power and Sample Size

Biomarker-guided clinical trial designs, which focus on testing the effectiveness of a biomarker-guided approach to treatment in improving patient health, have drawn considerable attention in the era of stratified medicine with many different designs being proposed in the literature. However, planning such trials to ensure they have sufficient power to test the relevant hypotheses can be challenging and the literature often lacks guidance in this regard. In this study, we focus on the parallel subgroup-specific design, which allows the evaluation of separate treatment effects in the biomarker-positive subgroup and biomarker-negative subgroup simultaneously. We also explore an adaptive version of the design, where an interim analysis is undertaken based on a fixed percentage of target events, with the option to stop each biomarker-defined subgroup early for futility or efficacy. We calculate the number of events and patients required to ensure sufficient power in each of the biomarker-defined subgroups under different scenarios when the primary outcome is time-to-event. For the adaptive version, stopping probabilities are also explored. Since multiple hypotheses are being tested simultaneously, and multiple interim analyses are undertaken, we also focus on controlling the overall type I error rate by way of multiplicity adjustment.

The need for immune biomarkers for treatment prognosis and response in genitourinary malignancies

Immune biomarkers encompass a wide range of blood-borne and cell-associated molecules whose detection or expression may change in response to an immune therapy. These immune therapies encompass a range of platforms including autologous cellular products, in other words, dendritic cells, prime boost DNA vaccines, chimeric antigen receptor (CAR) T cells and checkpoint inhibitors. The response to checkpoint inhibitors by a particular cancer may not be necessarily associated with a change in a particular immune biomarker; other immune biomarkers are needed to assess their association with treatment response or a change in the biology that can impact on the immunologic milieu. How these potential biomarkers can be incorporated into clinical trial design, and their role in interrogating the immunologic milieu will be discussed.

Perspectives on the integration of Immuno-Oncology Biomarkers and drugs in a Health Care setting

Immunotherapies, specifically checkpoint inhibitors, are becoming an important component in cancer care with the most application now in melanoma and lung cancer patients. Some drawbacks that converge with this new evolution are the rather low response rates to these drugs and their high cost with a significant economic impact on the health care system. These major challenges can likely be circumvented by implementing a "personalized immuno-oncology" approach to accomplish a selection of optimal responders based on biomarkers. In this paper we first discuss the legal framework for the development of valuable in vitro diagnostics. Based on a case study in lung cancer, the clinical validity and utility requirements of predictive immuno-oncology biomarkers is highlighted and an overview is given on the evolution towards multiplex or omics-based assays together with its challenges and pitfalls. Finally, some initiatives between the public and private sector are pinpointed to sustain the future access to innovative medicines in cancer therapy at a reasonable cost.

Sex-Divergent Clinical Outcomes and Precision Medicine: An Important New Role for Institutional Review Boards and Research Ethics Committees

The efforts toward individualized medicine have constantly increased in an attempt to improve treatment options. These efforts have led to the development of small molecules which target specific molecular pathways involved in cancer progression. We have reviewed preclinical studies of sunitinib that incorporate sex as a covariate to explore possible sex-based differences in pharmacokinetics and drug–drug interactions (DDI) to attempt a relationship with published clinical outputs. We observed that covariate sex is lacking in most clinical outcome reports and suggest a series of ethic-based proposals to improve research activities and identify relevant different sex outcomes. We propose a deeper integration of preclinical, clinical, and translational research addressing statistical and clinical significance jointly; to embed specific sex-divergent endpoints to evaluate possible gender differences objectively during all stages of research; to pay greater attention to sex-divergent outcomes in polypharmacy scenarios, DDI and bioequivalence studies; the clear reporting of preclinical and clinical findings regarding sex-divergent outcomes; as well as to encourage the active role of scientists and the pharmaceutical industry to foster a new scientific culture through their research programs, practice, and participation in editorial boards and Institutional Ethics Review Boards (IRBs) and Research Ethics Committees (RECs). We establish the IRB/REC as the centerpiece for the implementation of these proposals. We suggest the expansion of its competence to follow up clinical trials to ensure that sex differences are addressed and recognized; to engage in data monitoring committees to improve clinical research cooperation and ethically address those potential clinical outcome differences between male and female patients to analyze their social and clinical implications in research and healthcare policies.

The Importance of Biopsy in the Era of Molecular Medicine

Recent advances in the molecular characterization of cancers have triggered interest in developing a new taxonomy of disease in oncology with the goal of using the molecular profile of a patient's tumor to predict response to treatment. Image-guided needle biopsy is central to this "precision medicine" effort. In this review, we first discuss the current role of biopsy in relation to clinical examples of molecular medicine. We then outline important bottlenecks to the advancement of precision medicine and highlight the potential role of image-guided biopsy to address these challenges.

Bayesian Baskets: A Novel Design for Biomarker-Based Clinical Trials

Purpose

Biomarker-based clinical trials provide efficiencies during therapeutic development and form the foundation for precision medicine. These trials must generate information on both experimental therapeutics and putative predictive biomarkers in the context of varying pretrial information. We generated an efficient, flexible design that accommodates various pretrial levels of evidence supporting the predictive capacity of biomarkers while making pretrial design choices explicit.

Methods

We generated a randomization procedure that explicitly incorporates pretrial estimates of the predictive capacity of biomarkers. To compare the utility of this Bayesian basket (BB) design with that of a balanced randomized, biomarker agnostic (BA) design and a traditional basket (TB) design that includes only biomarker-positive patients, we iteratively simulated hypothetical multiarm clinical trials under various scenarios.

Results

BB was more efficient than BA while generating more information on the predictive capacity of putative biomarkers than both BA and TB. For simulations of hypothetical multiarm trials of experimental therapies and associated biomarkers of varying incident frequency, BB increased power over BA in cases when the biomarker was predictive and when the experimental therapeutic worked in all patients in a variety of scenarios. BB also generated more information about the predictive capacity of biomarkers than BA and categorically relative to TB, which generates no new biomarker information.

Conclusion

The BB design offers an efficient way to generate information on both experimental therapeutics and the predictive capacity of putative biomarkers. The design is flexible enough to accommodate varying levels of pretrial biomarker confidence within the same platform structure and makes clinical trial design decisions more explicit.

Challenges and methodology in the incorporation of biomarkers in cancer clinical trials

Biomarkers can be used to establish more homogeneous groups using the genetic makeup of the tumour to inform the selection of treatment for each individual patient. However, proper preclinical work and stringent validation are needed before taking forward biomarkers into confirmatory studies. Despite the challenges, incorporation of biomarkers into clinical trials could better target appropriate patients, and potentially be lifesaving. The authors conducted a systematic review to describe marker-based and adaptive design methodology for their integration in clinical trials, and to further describe the associated practical challenges. Studies published between 1990 to November 2015 were searched on PubMed. Titles, abstracts and full text articles were reviewed to identify relevant studies. Of the 4438 studies examined, 57 studies were included. The authors conclude that the proposed approaches may readily help researchers to design biomarker trials, but novel approaches are still needed.

Biomarker-Guided Non-Adaptive Trial Designs in Phase II and Phase III: A Methodological Review

Biomarker-guided treatment is a rapidly developing area of medicine, where treatment choice is personalised according to one or more of an individual’s biomarker measurements. A number of biomarker-guided trial designs have been proposed in the past decade, including both adaptive and non-adaptive trial designs which test the effectiveness of a biomarker-guided approach to treatment with the aim of improving patient health. A better understanding of them is needed as challenges occur both in terms of trial design and analysis. We have undertaken a comprehensive literature review based on an in-depth search strategy with a view to providing the research community with clarity in definition, methodology and terminology of the various biomarker-guided trial designs (both adaptive and non-adaptive designs) from a total of 211 included papers. In the present paper, we focus on non-adaptive biomarker-guided trial designs for which we have identified five distinct main types mentioned in 100 papers. We have graphically displayed each non-adaptive trial design and provided an in-depth overview of their key characteristics. Substantial variability has been observed in terms of how trial designs are described and particularly in the terminology used by different authors. Our comprehensive review provides guidance for those designing biomarker-guided trials.

Precision medicine in cardiology

The cardiovascular research and clinical communities are ideally positioned to address the epidemic of noncommunicable causes of death, as well as advance our understanding of human health and disease, through the development and implementation of precision medicine. New tools will be needed for describing the cardiovascular health status of individuals and populations, including 'omic' data, exposome and social determinants of health, the microbiome, behaviours and motivations, patient-generated data, and the array of data in electronic medical records. Cardiovascular specialists can build on their experience and use precision medicine to facilitate discovery science and improve the efficiency of clinical research, with the goal of providing more precise information to improve the health of individuals and populations. Overcoming the barriers to implementing precision medicine will require addressing a range of technical and sociopolitical issues. Health care under precision medicine will become a more integrated, dynamic system, in which patients are no longer a passive entity on whom measurements are made, but instead are central stakeholders who contribute data and participate actively in shared decision-making. Many traditionally defined diseases have common mechanisms; therefore, elimination of a siloed approach to medicine will ultimately pave the path to the creation of a universal precision medicine environment.