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Real World Evidence for Precision Medicine
Amrit Ray, Global President, Research, Development and Medical, Pfizer Inc. and Kelly H. Zou, Vice President, Head of Medical Analytics & Insights, Research, Development and Medical and Sabina Ewing, Vice President, Head of Business Technology, Finance and Business Operations, Pfizer Inc.
The goal of precision medicine is right patient, right medicine, right time
A Changing Landscape for Bioinformatics in NCD Care
According to the National Institutes of Healthin the United States (U.S.), “bioinformatics is the branch of biology that is concerned with the acquisition, storage, and analysis of the information found in nucleic acid and protein sequence data.” Historically, there was a limited use and reliance on bioinformatics. Nowadays, with the explosion of RWD, such as electronic health records (EHR), affecting and improving many areas of health care, we have more opportunity to harness bioinformatics to guide drug development.
Patient-centric bioinformatics tools can be applied and embedded to the entire drug development process, starting from molecule design and going through to product development, formulation enhancement and health technology assessments. Relevant tools include machine learning, data and text mining, predictive modelling, and artificial intelligence. These tools are particularly apt for NCDs, which are now the leading cause of death around the world, killing 41 million people per year, equivalent to 71% of all deaths globally, with more than three quarters (32 million) occurring in low- and middle-income countries. Bioinformatics has a high applicability as we seek to tackle the public health challenges of NCDs.
Real World Evidence
Real-world evidence (RWE) has been defined in the 21st Cures Act, and its framework and guidance documents have been proposed by the Food and Drug Administration. RWE is generated and leveraged through the appropriate data analysis and can be especially important in drug development. According to the U.S. Food and Drug Administration, specifically, RWE is the clinical evidence regarding the usage and potential benefits or risks of a medical product derived from analysis of RWD. RWE can be generated by different study designs or analyses, including but not limited to, randomized trials, including large simple trials, pragmatic trials, and observational studies (prospective and/or retrospective).
In the pharmaceutical industry, RWD may be used for a variety of purposes, including but not limited to the following areas: drug discovery to support phenotyping of patient populations in terms of rapidity of disease progression and responses to therapy; clinical development to aid in optimizing randomized controlled trial design and identifying investigator sites; medical affairs to support to directly assess patient experiences using patient reported outcomes and the safe and appropriate use of medicines; patient centricity to demonstrate the value of their medicines to both patients and health care providers; regulatory requirements to monitor medication safety; commercial development to provide more granular information on treatment usage, persistency, and adherence; business development to inform forecasting models.
The goal of precision medicine is right patient, right medicine, right time. To identify the “right patient,” both cross-sectional and longitudinal RWD are useful. To develop the “right medicine,” especially in certain disease areas such as the rare disease space, administrative claims data may help assess and optimize health care providers’ therapeutic decisions, monitor adherence, assess gaps in therapies, and evaluate and reasons switches among different dose levels and therapeutic options. The health care industry puts patients first to drive innovations and to develop the most appropriate medicines and treatment courses. To capture the “right timing” for treatment, both real-time instantaneous and longitudinal data are necessary. RWD, typically in the form of big data, are characterized by volume, velocity, variety, veracity and beyond. Large volume is only one aspect of the data providers and patients deal with. Thus, the right analytic strategies will require increased computing power, business technological resource, as well as domain and analytical expertise.
The ultimate goal of precision medicine (PM) is “right patient, right medicine, right time.” To identify the “right patient,” both cross-sectional and longitudinal real-world data (RWD) are useful. Examples of RWD include electronic health records (EHRs), claims, patient reported outcomes (surveys, preferences), genomic, images, and laboratory.
Recently, the US Food and Drug Administration (FDA) established a strategic framework to advance the use of real-world evidence (RWE) to support development of drugs and biologics.
Here, several experts from pharma, biotech, government, academia, and technology providers discuss the opportunity to use such big data to find the right drug at the right time using the right target, which is ripe for forming collaborative partnerships.
A Vision for the Future
The triad of bioinformatics, precision medicine and RWD offer tremendous possibilities to fully translate data and insights and meaningful health outcomes, particularly in the context of evolving public health needs such as NCDs. As artificial intelligence, machine learning, natural language processing, health mobile apps, and other capabilities mature, we will see an opportunity for convergence of previously disparate data and previously discrete tools into integrated opportunities for impact. For us to seize the opportunity, it will be necessary to harness computational power, artificial intelligence, and high volumes of data in an efficient way. It is imperative that qualitative and quantitative skill sets, and experts from across the board, including policy and decision-makers, providers, payers, strategists, and data scientists, do not fragment their efforts and instead come to the table together to make these possibilities a reality.
The views and opinions expressed in this article are the authors’ own and should not be attributed to Pfizer, its directors, officers, employees, or affiliates, or any organization with which she is employed or affiliated. No Pfizer-owned or licensed data were used. No writing or editorial support was provided.