Guest Column | July 11, 2019

Clinical Trials In The Era Of Precision Medicine: What Needs To Change?

By John Whyte, M.D., WebMD, and James Gillespie, Ph.D., Saint Mary’s College


In Part 1 of this two-part article, we examined the implications the transition to value-based, patient-centric, precision medicine has for drug discovery, development, and deployment, particularly for clinical trials. For a key component of this shift, many biopharmaceutical companies and clinical research organizations (CROs) are integrating patients into their processes. As it grows in importance, precision medicine is facilitating this integration and creating a platform for improved clinical trials and medical care.

In Part 2, we explore precision medicine in more depth regarding the tailored and targeted treatments that aim to match patients to medicines according to particular clinical, demographic, and lifestyle factors. We note that data from a rich variety of sources, including real-world data (RWD), underpins the ability to develop and deliver specific medicines for specific patients. Finally, we consider how the twin dynamics of patient-centricity and precision medicine will require biopharmaceuticals and CROs to redesign and transform their processes for converting molecules to medicines for patients.

Tailored And Targeted Treatments

Precision medicine requires viewing each patient as an individual rather than an average, random sample, and it helps get the right medication to the right patient at the right time. Precision medicine holds the potential to substantially enhance efficacy and outcomes by integrating disease-specific factors, patient-specific factors, environmental/sociocultural considerations, quality of life and functionality considerations, and economic outcome analyses. One goal is to effectively stratify at-risk patients for targeted prevention and treatment. Precision medicine involves several steps: developing a therapeutic hypothesis, determining which patients respond and why, and creating robust mechanisms for identifying those patients. The process includes thinking about delivery of the drug, the biomarker response, and the diagnostic tool for identifying the proper patients. As therapies are identified based on analytics, clinical trials and medical treatment can become more effective, efficacious, and efficient.

Based on the metabolic profiles of individual patients, drug makers can optimize safety assessment dose escalation studies. By examining the results from a set of N-of-1 studies, researchers may be able to analyze the effectiveness of an interaction in groups where members share a particular gene. The identification of the subpopulation most responsive to a drug will serve to narrow indications, and precision medicine can help providers ensure the patients are properly metabolizing drugs, which could result in correct dosing and fewer side effects.

In order to create a platform for precision clinical trials of target therapies, we need to develop assessments of genetic factors, environmental impacts, and gene-environmental interactions. Leveraging evidence from genes, the environment, and patient lifestyles, precision medicine generates courses of disease prevention and treatment. More specifically, patients are grouped by biomarkers, clinical features, demographics (e.g., age, race, sex), disease subtypes, family history, genomics, health history, lifestyle (e.g., alcohol, exercise, nutrition, smoking, weight), molecular subpopulations, preferences, risk profiles, and socioeconomics.

Applicable areas for precision medicine include epigenomics, metabolomics, pharmacogenomics, and proteomics. Pharmacogenomics is an increasingly useful field for defining variables such as heredity to explain and predict variability of drug responses. With more information early on regarding disease biology, we can enhance our characterization of individual disease biomarkers. There are individual differences in the risk of genetic diseases, as well as patient-specific differences in genetic polymorphisms and mutations in drug metabolizing enzymes, receptors, toxicity targets, and transporters. Researchers leverage this information to design trials with drugs targeting specific disease markers, and the treatment options will vary depending on severity (e.g., chronic vs. immediately life threatening) and demographics/genomics (e.g., individual rates of metabolism).

Lifestyle-driven chronic diseases are reaching epidemic proportions in some regions, so, by necessity, we are shifting from disease care to healthcare that places more emphasis on prediction and prevention. Precision medicine holds the potential to be personalized (essentially by definition), predictive, and preventive. Precision medicine can help improve preventive care for chronic ailments and to characterize disease states in as many dimensions as possible. Disease interception strategies will become increasingly prominent; this involves intervening earlier in the point of diagnosis and seeking solutions to reverse or inhibit progression of the disease. Precision medicine can help establish a learning health system and create increased accountability and responsibility for patients and caregivers, which extends to clinical trials.

Disease progress is dynamic; there is a complex interplay between patient variations, provider capabilities, and treatment outcomes. The patient’s unique profile serves as a starting point for comparing alternative treatments and expected outcomes, and each individual patient has very distinct characteristics in stage and secondary disease-specific metrics, which impacts decisions regarding prognosis and treatment.

Underrated: Data From The Real World

Data is the foundation for patient-centric, precision medicine in clinical trials. The life sciences industry has historically relied almost exclusively on randomized controlled clinical trials for data, while making little use of real-world data (RWD) from patients. In some ways, existing patient data is essentially a huge complete clinical trial. This data can be normalized to facilitate the search retrospectively for benchmarks and trends that can be leveraged to improve care. By collecting, organizing, and extracting from existing data sets, clinicians and researchers can assess how specific sets of patients reacted over several years.

Data can be utilized from direct and indirect sources to achieve a more holistic perspective on each patient, so there is now an enormous volume of qualitative and quantitative data for advancing precision medicine. We are gaining RWD for claims data, genomic data sets, electronic health records (EHRs), electronic medical records (EMRs), imaging systems, mobile apps, personal health records (PHRs), and wearables. By collecting a large volume of data over a lengthy period of time, the goal is to demonstrate whether some treatments work more effectively in some subpopulations than others. The growth in data has given rise to a whole subindustry of highly accomplished companies specializing in RWD aggregation. These third-party RWD aggregators can be leveraged to help shift to the new paradigm of data-driven clinical trials based on precision medicine. Across the industry, there is still relatively low integration of RWD with decision-making on protocols, processes, and patient selection. It is evident that CROs and pharmaceutical companies still need to develop a RWD road map.

By using patient data to develop novel treatment approaches, researchers can achieve significant healthcare advancements. However, this should be accompanied by new thinking regarding patient consent on whether and how their data is used. Patients must have confidence that their data will be used properly. Data transparency is essential to establish confidence and trust from patients as privacy and security for patient data is more important now than ever.

Process Redesign And Transformation

The biopharmaceutical industry, including clinical trials, is undergoing a profound transformation. We are at the beginning of the era of patient-centric, value-based medicine. Because there are more pay-for-performance agreements between payers and pharmaceuticals, the industry needs to redesign how it collects clinical evidence. Payers want to see therapies impact patients in clinically meaningful ways, so there is a need to sharpen the focus of clinical trials and design them to demonstrate meaningful impact on well-characterized and well-understood disease parameters.

Because of key efficacy and safety criteria related to stringent regulatory standards, the standard scientific clinical trial is not going to become obsolete anytime soon. Yet, clinical trials are moving beyond narrow snapshots gathered when subjects visit trial sites; instead, companies are beginning to create a continuous learning algorithm involving clinical trials and real-world experience. In this regard, precision medicine can quicken development timelines and enhance R&D productivity as it becomes increasingly embedded in the clinical trial and drug development processes. One key is to develop deep biological insights early in the drug development process, so companies are revising their criteria for determining whether to enter a drug candidate into a human trial and then into typically large, expensive Phase 2 and Phase 3 studies.

Precision medicine is taking us into new spheres of decision-making, with the potential to truly transform diagnosis and treatment. Instead of just treating the symptom or even the condition, we can now address the underlying genetic causal mechanisms. Precision medicine holds the potential to reduce the “background noise” of clinical trials, including enabling smaller trial sizes, speeding time to market, and increasing success probabilities. The N-of-1 trials propose replacing large-scale trials of whole groups with methodical study of individual patients. However, the requirement to provide specific treatment to different subgroups of patients will make clinical trials more complex, so the industry needs to redesign how it interacts with patients. CROs will need to establish expert teams to structure and run precision-medicine-oriented trials for their sponsor clients.

Precision medicine will profoundly change what research is performed and how knowledge is created, while requiring a multidisciplinary approach to clinical trials. Companies can be the crucibles for converting information and insights into tangible solutions for patients. The ability of CROs and biopharmaceutical companies to bring all stakeholders together to work on innovative treatment options will be of increasing importance. Companies should embrace the crucial role of joining the top experts and external partners to work collaboratively to advance new therapies for patient needs in the spirit of continuous learning. As the industry progressively integrates precision medicine into the mainstream clinical workflow, it will have a transformative impact on clinical trials, and we will develop novel therapeutic solutions to extend and improve the lives of patients.

About the Authors:

John Whyte, M.D., is currently the chief medical officer at WebMD. In this role, he leads efforts to develop and expand strategic partnerships that create meaningful change around important and timely public health issues. Prior to WebMD, Whyte served as the director of professional affairs and stakeholder engagement at the FDA’s Center for Drugs Evaluation and Research, where he worked with healthcare professionals, patients, and patient advocates to provide them with a focal point for advocacy, enhanced two-way communication, and collaboration.

James Gillespie, Ph.D., is a faculty member in the Department of Business and Economics at Saint Mary’s College, where he focuses on international management and new ventures. His experience includes the Center for Healthcare Innovation in Chicago, the Stanford University School of Medicine’s Clinical Excellence Research Center, the Yale University School of Medicine’s Center for Digital Health & Innovation, and Chief Strategy Officer at MyKaren. Gillespie’s education includes Carnegie Mellon University, Harvard University, Massachusetts Institute of Technology, Northwestern University, and Princeton University.