Interview with Dr Kelly H. Zou

Alongside your role in Pfizer, you are a well-respected leader in Statistical Science, including being an elected fellow of the American Statistical Association and publishing approximately 140 academic articles. With the onset of advanced data analytics in drug development, how are approaches in statistical science changing?

kelly zou pfizer precision medicineIn drug development, the bread and butter are clinical pharmacology through pharmacokinetic (PK)/pharmacodynamic (PD) modelling and clinical trials through designs, executions, safety-monitoring, and reporting.

  • The emergence of electronic medical records (EMR) or electronic health records (EHR), medical and pharmacy claims, and omics (e.g., genomics, proteomics, metabolomics and glycomics) data calls for sophisticated data management and statistical modelling.
  • Health economics outcomes research (HEOR), health care policy analysis, bioinformatics, medical imaging, and genomic science are thriving. Therefore, data science and statistical sciences have enabled advanced and complex approaches, such as machine learning, data and text mining, predictive modelling, and artificial intelligence.  

By giving research teams the ability to transform real-world data sources into evidence, how will this drive more efficiency in drug development?

  • Real-world evidence (RWE) has been defined in the 21st Cures Act in the United States. RWE is generated and leveraged through the appropriate real-world data (RWD), can be extremely important to support the entire product lifecycle.
    (On RWE: https://www.congress.gov/114/bills/hr34/BILLS-114hr34enr.pdf; on RWD: http://www.valueinhealthjournal.com/article/S1098-3015(10)60470-6/fulltext; on Big Data: http://www.ibmbigdatahub.com/infographic/four-vs-big-data )
  • For both efficacy and efficiency in drug development, RWD may be used for many purposes, including but not limited to the following:
  • Regulatory requirements under the 21st Cures Act (to monitor medication safety, including active pharmacovigilance, seeking out treatment interactions or identifying points in treatment where patients are likely to discontinue use of therapy.).
  • 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).
  • Commercial development (to provide more granular information on treatment usage, persistency, and adherence leading to more accurate forecasting model).
  • Business development (to inform forecasting models for acquisitions).
  • Medical affairs (to support the safe and appropriate use of medicines, to apply the findings from RWD analysis for Research and Development, and to demonstrate the value of their medicines to payers).

If our vision of precision medicine is to deliver diagnostic tests, to identify responsive patients, alongside new targeted therapies, what pressure will this put on development resources, including the unprecedentedly large volumes of data early in preclinical and clinical testing?

  • The ultimate goal of precision medicine is “right patient, right medicine, right time.” Let’s think about how RWD can help with these three components.
  • To identify the “right patient,” both cross-sectional and longitudinal RWD are useful. The types of data are ranging from EHRs, claims, surveys, preferences, genetics, images, and texts. The challenge would be to derive algorithms to screen patients and to find diagnostic and prognostic predictors.
  • 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 patient 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 big data’s 4 V’s: volume, velocity, variety and veracity. Large volume is only one aspect of the data providers and patients deal with. Thus, the right analytic strategies will require increased resource and expertise.

What is the importance of precision medicine in the new value-based care environment?

Precision medicine gets to the heart of maximizing values to patients by focusing on multiple aspects: right patient (individual, subgroup, cohort and population based), right medicine (better care), and right time (lower costs with more targeted and efficient therapies).

What conversations and who are you hoping to meet at the Big Data in Precision Medicine Summit in the fall in Washington DC?

As an Analytic Science Lead in Real World Data & Analytics, I hope to engage in subject-matter and data science conversations. As a member of the Patient & Health Impact group at Pfizer, I also hope to have shared knowledge on how RWE could lead to better patient health and higher business and scientific impact.

I also look forward to meeting and networking with qualitative and quantitative experts, including policy and decision-makers, providers, payers, strategists, and data scientists.


Kelly H. Zou is an employee of Pfizer Inc. Views and opinions expressed in this interview are the Dr. Zou’s own and do not necessarily reflect those of Pfizer Inc.

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