Meet Ovation: Pauline Gee

Hear from Pauline Gee, VP of Clinical Affairs, about her journey from research in animal models to human genomics at Ovation

How I got interested in personalized medicine


When I was in graduate school, I came home for a few days over the Christmas holidays to find out my mom had an infection for which she was taking Ciprofloxacin. Before going back to school, I had instructed my mom to finish the prescription even if she was feeling better as routine with antibiotics. It turns out that Ciprofloxcin initiated hepatotoxicity and had become noticeably jaundiced a day or two later when my brother checked on her. Had it not been discovered Ciprofloxicin would have destroyed her liver by activating a latent Hepatitis B infection. It was then I realized how some drugs were seriously contraindicated for certain individuals and that drugs were not just condition related but that each patient’s makeup was unique. We now know that a patient’s genetic makeup plays a large part in whether a drug is beneficial or noneffective or toxic. This experience taught me that a treatment strategy should be linked to personalized and precise information about patients’ safety and efficacy for targeted therapies based on genetically based mechanisms, and inspired me to go into the precision medicine field.


My path to human genomic data


I went on from there to do a medical research fellowship at UC Berkeley where I studied mechanisms of DNA damage which led to patented genetically engineered strains of bacteria, a version of the Ames test. I took these strains into a startup company that commercialized the Ames test for high throughput screening. After starting in R&D, I was part of the executive team that took the company public and eventually became its CEO and led its acquisition by a chemistry company that was synthesizing compounds for pharma companies.


Shortly afterwards, I went on to work for an international CRO that created a multiomics database that included gene expression, histopathology images, DMPK and many more relevant measures in rats from 2 doses of 450 marketed drugs that we followed for 5 time points. This database ultimately led to the FDA accepting gene expression data in NDA filings because gene expression revealed metabolic pathways induced by drugs both toxic and beneficial that would have been missed otherwise. When we did these analyses, it revealed drug interactions previously unknown. The power of genomics showed drug interactions with metabolic pathways could drive efficacy and toxicity demonstrating the power of genetics in drug development. Current strategies in drug discovery and development take into account not only genomics but also other ‘omics’ very early on.


My journey at Ovation


I decided to come to Ovation three and a half years ago because I was excited about the prospect of generating and providing human multiomics data to help life science companies to achieve similar insights about human populations that we found initially in our rat database. Many people don’t respond to drugs even though we see clinical efficacy in a randomized controlled trial. We know that people’s genetic makeup and physiological status play a big part in whether a drug will be successful or not in achieving meaningful health outcomes, but getting that data, especially in diseases like NASH or Cardiovascular disease, where we don’t have genetic testing as part of clinical care, has been extremely challenging.


At Ovation, we have been banking remnant clinical samples that would have been discarded and obtaining genomic data from them. It’s been amazing to help realize Ovation’s vision for salvaging data from waste samples. Because of the capability in our network of labs, we have representation from diverse geographic regions and diverse ancestry within the U.S. As part of our vision for the future, we see the potential to help clinical labs and life sciences co-develop companion diagnostics based on data that we generate with our network of labs.


See for yourself with our NAFLD Whole Genome with Linked Clinical Data sample available on the Amazon Data Exchange. We were able to achieve 50x depth of coverage for these samples. From here, there’s only room to grow – and I’m excited to drive this effort.

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