In an age where big data is driving advances in the pharmaceutical industry, and sharing of clinical data is ever more important, Pfizer’s Alexandria Papa is helping to usher in the data revolution.
Her title at Pfizer: Senior Associate Business Analyst, Research Early Clinical Development Business Technologies
What it means: With modern analytical tools, clinical trials are producing an unprecedented volume of raw data. It’s a treasure trove that can potentially deliver key insights essential to the process of creating new and better medicines, but in its unrefined format, the data is overwhelming and virtually unusable. Alexandria’s job is to design the software systems that help Pfizer scientists to find the needles within this haystack. It requires an in-depth understanding of biological and clinical study data as well as an appreciation for the goals of research programs. “We try to tease through and figure out how scientists intend to use their data and then structure the information to expedite data presentation and analysis,” she says.
“We have to understand the science and the software, translating between investigators and technologists. It makes me feel good to come in every day and be that person.”
How she got here: Alexandria showed an early love for science, and found her way into the lab at 14 years old. Her mother, a hairdresser, had a client who worked at the Marine Biological Laboratory at Woods Hole, Mass., who invited the young Alexandria to work there. She started sweeping floors and organizing journal articles. By 15, she was analyzing and interpreting gene sequences¬. By 19, she did her own gene sequencing— mapping out the entire genome of an organism. After earning her undergraduate degree in biochemistry and molecular biology, she knew a career in the lab wasn’t exactly the right fit, but wanted to shift her focus to contributing to the understanding of human genetics. She found her calling when pursuing a master’s degree in bioinformatics—the use of computer technology to understand and analyze biological data, particularly biomolecules such as genes, proteins, metabolites and RNA transcripts.
On Being a Data Wrangler: Scientists come to her team with clinical trial results and large amounts of data (such as lab results, demographics, and molecular ‘omics data, which include things like data on how frequently people express a particular gene). The amount of data is so large and variable that specialized software is often required. Her job is to figure out what type of data points they need, —out of billions— and then how to prioritize it, visualize it, and share it with other colleagues, potentially fostering new cross-departmental collaborations.
Seeing the Value in ‘Omics: An example of ‘omics data being put to good use is the molecular inclusion and exclusion criteria for clinical trials. A patient’s DNA may be sequenced before entering a trial to screen for the particular genetic variant that the drug has been designed to target. Or if the gene expression of a patient’s tumor is profiled, a more precise treatment may be identified for cancers matching that particular expression profile.
Supporting A Library of Genes: Her team helps support one of the largest Genome Wide Association Study (GWAS) libraries in the world, which helps scientists better understand the connection between genes and a range of diseases and traits.