Precision medicine is driven by advances in omics technologies that facilitate identification of mutations and the presence or absence of certain molecular targets, which, in turn, enables streamlined development of therapeutics that are precisely tailored to an individual’s specific disease characteristics. Biomarkers are integral to the development of precision medicines; in fact, a recent report revealed that biomarker-driven strategies increase the likelihood of drug approval by approximately 40 percent. To successfully integrate biomarkers into clinical design and execution, developers need a clear plan for bridging the gap from bench to bedside to ensure biomarker feasibility and utility.
Understanding Categories of Biomarkers
Biomarkers can take many forms, and their role in successful development of targeted therapies and personalized medicines is increasing. Biomarkers fall broadly into 3 categories:
- Safety. Preclinical biomarkers are often focused on safety, where correlations with toxicity—typically assessed through biochemical changes and histopathologic evidence of tissue injury—are evaluated and used to help define a drug’s therapeutic index and clinical dose. To be suitable for transition from bench to bedside, these biomarkers must be able to be monitored in a clinical-development setting.
- Predictive/prognostic. These are biomarkers that allow for monitoring of drug efficacy and pharmacodynamics. For hypothesis-free biomarker discovery, multiplexed measurements of analytes are used to define panels of potentially valid biomarkers for future application. A key consideration for this type of biomarker discovery is how to manage the resulting complex datasets to enable extraction of relevant molecular signatures for the biological process or drug mechanism of action (MOA) in question.
- Diagnostic. These are markers of specific disease, which are typically identified and validated by comparing well-characterized cohorts of ex vivo diseased human tissues with relevant healthy control tissues. An understanding of variations in biomarker expression among diseased vs nondisease populations can be effectively used to guide patient selection for clinical trials. Where expression of a particular biomarker or biomarker signature indicates that a patient’s disease characteristics are a match for the therapeutic MOA, this provides a higher likelihood of clinical response.
Assessing Biomarker Clinical Utility
To maximize its clinical utility, a biomarker must be monitorable, transferable, and reliable. Many biomarkers are initially identified and characterized from animal models and, as such, it is important to ensure that these biomarkers will be suitable for use in the ultimate clinical setting. In particular, biomarkers must correlate with a clinically relevant endpoint that, ideally, is minimally invasive to the patient. Moreover, it is imperative that the right sample type be collected and processed in the right way to allow for the downstream clinical biomarker assay to generate high quality, actionable data.
Selection of the assay type is also important, as utility will be circumscribed by accessibility. If an assay requires a highly specialized platform or fresh samples, it will be more challenging to deploy not only in a global clinical trial, but also, if being developed as a diagnostic, in routine clinical practice. Assay turnaround times must also be factored into biomarker feasibility and utility, especially if biomarker data are being used to make real-time clinical decisions such as patient inclusion or exclusion or changes to the administered dose. Considering the assay format, with respect to the number of analytes measured, is also key for the management, integration, and interrogation of multiomic data sets. In particular, it’s important to plan for how the interpretation of incidental findings from a multiplexed, nontargeted assay dataset is presented to regulators.
Planning the Preclinical-to-Clinical Transition
Planning for the transition of a biomarker from the preclinical to clinical setting should begin as early as possible during the development process and well before clinical collection plans are finalized and documented. The importance of early dialogue on what samples are required and why they need to be processed at a specific time in a particular way for downstream biomarker analyses cannot be overemphasized. This is an area where Precision for Medicine creates significant value through early discussions around the feasibility of various assay options and through custom assay development and validation.
Another critical element of early planning is considering how the resulting data will be used, as this will define the level of assay validation required by regulators. As assay development and validation can take several months, it is advisable to work backwards from the first patient in (FPI) date to ensure that the assay is ready in time to support the trial. Precision for Medicine has extensive experience in evaluating assays and assay panels and determining whether they are fit-for-purpose or require modifications—either in design or in the level of validation—to meet clinical study needs.
Ensuring Assay Practicalities Fit Within the Constraints of Clinical Trials
As mentioned above, many preclinical assays are developed in animal models, so it is critical to understand if and how a preclinical assay will translate to the reality of the clinical development environment.
Early communication between preclinical assay development teams and their clinical counterparts is essential for understanding the practicalities governing assays within the constraints of a global clinical trial setting. Key factors to consider include:
- Variability. The preclinical species comprises an inherently well-controlled population with less variable biological background than the clinical trial population, which will demonstrate significant inter-subject variability and, often, concomitant disease. Further, due to fundamental differences in biology between species, preclinical assays may need to be redesigned to suit the clinical biology
- Sample type and logistics. With preclinical animal model assays, fresh blood is typically collected and immediately processed on site, resulting in optimal sample quality and integrity and, thus, the best possible samples for downstream analyses. In clinical trials, however, patient samples will be collected from multiple, often global, sites and will then require shipment to a sample processing lab within a suitable timeframe for the downstream application. Careful consideration must be given to the location of clinical trial sites and the logistics of transporting precious samples to a processing lab in a safe and timely manner. Options for mitigating logistical challenges include identifying a CRO partner with global lab coverage, selecting a sample type, where appropriate, that is suitable for local processing and storage prior to batched analysis, or stabilizing the sample. Precision for Medicine has created an integrated solution for sample management, from collection through analysis, by designing and providing sample collection kits, supporting a worldwide network of specialty biomarker and sample processing labs, and developing a global infrastructure to support shipping, logistics, and storage
Optimizing Actionable Data from Biomarker Analysis
There is a broad array of platforms and technologies available for biomarker analysis, enabling interrogation of samples including nucleic acids, proteins, tissues, and biofluids, as well as measurement of cellular function. Platform selection will be driven by the assay type needed to generate the biomarker data necessary for on-trial decision making. The level of assay validation required is based upon the types of decisions those data will influence. For instance, assays used for patient selection or for an altered-dose regimen will require more comprehensive analytical and clinical validation.
While dose escalation through clinical cohorts is primarily driven by safety data and clinical observations or measurements, building in the right early or exploratory biomarker data can help drive decisions around adaptive trial design and confirm the utility and value of biomarker changes that can be applied as clinical development progresses. Here, the challenge lies in managing and making sense of all the resulting data. In addition to biomarker assay data, clinical trials produce a plethora of datasets in diverse formats from multiple sources across patients, sample types, and timepoints. Precision for Medicine has developed QuartzBio®, a powerful data platform that can collate, integrate, interrogate, and interpret these data, together with third-party, publicly available data sources, to generate actionable insights. This platform also includes a virtual sample inventory management tool that provides ongoing visibility into the location and status of clinical samples.
Conclusion
Preclinical data are a valuable and essential part of biomarker discovery, validation, and proof of concept. To successfully advance preclinical biomarker assays from the bench to the bedside, it is crucial to maintain engagement between discovery and clinical biomarker and operations teams and to develop a clear plan for delivering assays that can be utilized within the constraints of the clinical trial setting.
Precision for Medicine is focused on delivering personalized medicine approaches to match the right patient with the right drug at the right dose and time to optimize treatment impact and outcome. We support the entire development process, from design and execution of biomarker-driven clinical trials and kitting and logistics to specialty laboratory services, biomarker data management, and regulatory and commercialization support. Our goal is to provide comprehensive translational science-led research services to deliver biomarker science at scale.
Amanda Woodrooffe, PhD
Senior Vice President, General Manager UK Labs
Experienced leader of scientific operations for drug discovery in both the pharmaceutical and CRO industries. Driven to provide actionable data enabling biopharmaceutical research and development. Collaborative partner ensuring licensing and scientific support for corporate development initiatives.
1BIO, PharmaIntelligence, Quantitative Life Sciences. Clinical Development Success Rates and Contributing Factors 2011-2020. Available at https://pharmaintelligence.informa.com/~/media/informa-shop-window/pharma/2021/files/reports/2021-clinical-development-success-rates-2011-2020-v17.pdf.