Three specialists – Dr. Sarah Blagden, Associate Professor of Medical Oncology, University of Oxford; Maria Prendes, Head of Oncology at the Biomarker Solution Center; and Kamal Saini, Senior Medical Director of Oncology at Labcorp Drug Development – recently discussed this highly relevant topic in an educational webinar, which is now available as an on-demand recording.
This article shares some of the highlights of the well-attended webinar that covered the role of biomarkers in accelerating drug development timelines, the benefits and limitations of biomarker analysis and the characteristics of novel trial designs.
Accelerating Drug Development Timelines with Biomarkers
There is a clear notion in drug development: Many anti-cancer treatments are directed towards a specific population of patients, and may benefit among them, only those patients with a histologically/molecularly defined cancer. While biomarkers offer clear benefits during the drug development process, they also present some limitations. Obvious benefits include among others, eliminating the burden of treating patients who will not respond to a therapy (basing the judgement on the presence/absence of a specific predictive biomarker); or identifying mechanisms of acquired resistance to a particular therapy (introducing new possibilities for combination therapies with other therapeutic agents). Therefore, early-at-risk investment in biomarker development leads to compounds with better patient outcomes and stronger cases for reimbursement, while accelerating and making more efficient the drug development process.
Clinical biomarkers also pose some limitations, they require time and resources. In general terms, the drug development process and the biomarker identification occur simultaneously, but in reality, biomarker development often lags behind drug development: The majority of the efforts are applied to developing the new anti-cancer agent and subsequently, identifying biomarkers with predictive, prognostic and pharmacodynamic value for the therapy. However, there is an urgent need in a clinical trial to identify robust biomarkers as early as possible in the clinical development program. Otherwise the confidence in a putative biomarker performance may be insufficient to restrict the treatment of a new agent to a subgroup of biomarker-positive patients, delaying the drug development timeline.
Early identification of robust biomarkers
Extracting the greatest value of a predictive, prognostic or pharmacodynamic biomarker requires identifying it as early as possible in the clinical development process.
Prior to utilizing a patient’s biomarker information in clinical practice, the biomarker should demonstrate robustness, with both analytical validity (answers the question whether or not we should trust the results of a specific biomarker), and clinical validity (the results obtained from the test should be related to other clinical information), as well as clinical utility (a particular biomarker should be ultimately useful to improve a patients’ health).
When debating which tests can be used as platforms to measure biomarkers in a clinical setting, we need to consider that these tests can be developed and validated as “fit-for-purpose”: ranging from exploratory purpose to inclusion/exclusion criteria (primary/secondary objectives and endpoints in a clinical trial).
As a potential biomarker for stratification of patients is discovered, identified and characterized in the early drug development phase, its clinical fitness must also be evaluated to understand its robustness before it can support commercialization. Aligning the market authorization of the drug with approval of the biomarker test is paramount to avoid delays in clinical drug development.
Understanding the role of adaptive trial designs
There are different strategies to incorporate biomarkers into the clinical study design. These strategies have been previously classified as enrichment strategies and stratified designs. A third group of biomarker-guided treatment strategies gaining prevalence in the last few years includes adaptive designs.
In 2010, the U.S. Food and Drug Administration defined an adaptive design as a study that includes a prospectively planned opportunity for modification of one or more specified aspects of the study design and hypothesis based on analysis of (usually interim) data from subjects in the study.
Adaptations to the design based on interim analysis include:
- Adding or dropping treatment arms
- Changes in the required sample size
- Changes in the allocated proportion of the study population in order to randomize more patients to treatment arms which are doing better
- Refinement of the existing study population according to their predictive biomarkers
The most common adaptations during the implementation of adaptive designs refer to changes in randomization probabilities within the biomarker-defined subgroups or dropping a biomarker defined subgroup.
Adaptive designs constitute a fundamental alternative in the era of precision medicine, allowing flexibility during the course of the trial. This type of design demonstrates cost benefits, and minimizes the required time to reach conclusive results (despite an increase sometimes in the number of subjects screened for the trial). Besides being more ethical towards patients by providing more efficient treatments, it also avoids erroneous conclusions.
Adaptive designs offer a good alternative for clinical trials when:
- The candidate biomarker is not known at the beginning of the trial
- There are multiple experimental treatments and pre-specified biomarker-defined subgroups
- Existence of well-stablished analytical validity
- Rapid turnaround time for biomarker assessment
In conclusion, the efficiency of a trial design is measured by the increased power of the study and the minimized required sample size or duration. Biomarker-driven adaptive design trials play an important role in reducing the cost and increasing the clinical utility of trials evaluating biomarker-guided treatment strategies.
Exploring complex innovative design (CID) trials
As the use of biomarkers is leading to fragmented, molecularly defined subpopulations, there is a greater need for trial designs that incorporate advances in biomarkers, statistics, and technology to address the inefficiencies of cancer drug development. As discussed in the Hoadley et al. Cell article, a multi-omics approach with a comprehensive and integrated molecular analysis can led to a definition of molecular relationships across a diverse set of human cancers and have potential clinical utility for cancer treatment trials.
Complex innovative design (CID) trials terminology comes from a U.S. FDA program that started in 2018 and runs until 2022 with the intent of advancing and modernizing drug development. A CID emphasizes the use of external data or the real-world data. It can include pre-specified adaptations to multiple aspects of the study, adaptive or Bayesian designs that require simulations. A CID trial focuses on the patient benefit and filling a gap where new therapies are needed for underserved patient populations.
Beyond the U.S., regulators are encouraging innovation and complexity in trial design. Both the European Medicine Agency (EMA) and MHRA have indicated that the regulatory perspective is tilted in favor of trying out new innovations as quickly as possible and adding CID as part of a balanced portfolio of cancer studies.
Watch the on-demand webinar to hear more about biomarkers, with a case study describing a Phase III study design strategies based on biomarkers and recent examples of overcoming hurdles in conducting Complex Innovative Design (CID) studies.