QPP remains at the heart of model based drug development. Short for Quantitative Pharmacology & Pharmacometrics, it refers to several types of quantitative modeling including meta-analysis, PK/PD, statistical modeling and the modeling of go-no-go decision rules. Cytel’s expert Quantitative Pharmacology and Pharmacometrics group delivers high quality solutions to help our customers get those decisions right.
In this blog we talk to Tina who lives in Stonington, Connecticut, to find out more about her career path, current role at Cytel, and her interests outside of work.
What sparked your interest in Quantitative Pharmacology & Pharmacometrics, as a career?
I had been working in the lab and using a compound that my colleagues had recommended as a positive control for my experiments – except it wasn’t working and it was invalidating all of my experiments. I started to suspect that because the chemists had slightly modified the formulation, I would need to modify the dose in order to get it to work. My mentor recommended that I used a mathematical approach to target my dose instead of starting with an entire new series of experiments. He taught me how the pharmacokinetic equations worked together and in a single experiment, I was able to show that the dose that our modeling had predicted was the right dose for the new formulation! I was hooked… and from that point forward, I decided that I wanted to incorporate as quantitative approaches into my working practices from that point forward.
What is your current role?
I am a pharmacometrician in the strategic consulting group. Like the other strategic consultants, I help clients identify places in their programs where quantitative approaches could increase their confidence in the decisions that they have to make. My role is a little different because I help teams understand how the test drug is driving the results by taking into account our underlying physiology. My work tends to specifically focus on questions about the interactions between the drug and the body and how these interactions could impact the doses selected for the general population.
What functions do you collaborate closely with? Why are these relationships important?
Aside from working with the other consultants, we work closely with the programmers because we use the actual study data to support our analyses. The information we need comes from multiple sources and we often depend on a programmer to merge and format the data in ways that make sense for our purposes and with our software.
We also work closely with the phase 1 NCA team and the phase 1 group. Most drug companies use phase 1 studies to learn about the pharmacokinetic properties of their drugs. When a client approaches us for pharmacometric support, we review their protocols to ensure that the design supports pharmacokinetic analysis. Often the NCA group will be responsible for actually conducting the analyses that are reported in the clinical study reports, while any additional pharmacometric models will be reported independently. Therefore, our two groups share information and decisions so that any single team will have consistent, comprehensive PK deliverables.
Another group that we work closely with is the outcomes research database group. This group is responsible for creating databases that contain data reported in the literature and at regulatory sites like clinicaltrials.gov. Because they compile data across multiple sources, they often have to make decisions about how and when to pool the data. Sometimes, they need support from an analyst to make sure that the decisions they make during database development are appropriate with regard to the planned analyses. Conversely, we may need to work with them to understand how any given field was populated or where we can find information inside the database. Both of our teams enhance each other even though we do slightly different things.
How do you see the role of the Pharmacometrics becoming more influential within drug development?
Many of the models we build support simulation into different settings. For example, we can use a model to simulate how a person might respond to a different dose or what might happen if a person misses a dose. Drug developers can use this information to increase their confidence that any given study will be a technical success. They can also use it to eliminate non-informative tests and to select the doses that are most likely to be of benefit for their patient populations. This usually has a direct impact on the drug development process because less time and money is wasted on treatments that are unlikely to work.
Regulators use these models to help address concerns that the sponsors have not completely addressed within their submission documents or to understand probable responses across multiple drug products in real world situations. Globally, regulatory agencies share a commitment of reducing patient exposure to doses of drugs that are not likely to provide benefit, so whenever a model can be safely applied in lieu of a full blown clinical trial, the agencies would prefer to take that approach. A model can often help them make a decision more quickly than a clinical trial or to enable them to give some guidance while waiting for more information to be collected.
How do these skills and technical competencies add value in drug development today?
In the past, we didn’t have the technology to regularly use quantitative approaches during drug development. We weren’t always able to assay the drugs the way we can now. And even if we could assay the drug, we didn’t always have the computational power to run the equations of most interest. That has been changing and is continuing to develop which now means that in many instances where a team would have had to use an educated guess, they can apply the knowledge to increase their confidence that the right decisions are being made.
From a humanitarian standpoint, this spares patients by using the model-predicted information to focus the studies in ways that make the most sense. It can shave years off of drug development time which means that a good drug can make it to the market more quickly and it reduces the likelihood that an ineffective drug will be approved. From a financial standpoint, companies can de-risk their budgets by investing in the studies that are well-designed for the drug and patient population of interest.
What are the main challenges you face in the role?
The logistics for doing this kind of work can sometimes be kind of tough. We use observed data to build our models and usually the drug concentration data is the last set of data to arrive after a study finishes. The study team is usually waiting on our results to get a complete understanding of what happened in their study and to finalize the plans for the next set of clinical trials. We are often working to a tight timeline to complete the work and if there are any un-foreseen delays, the benefit of the modeling can become minimized because a team may have needed to make a decision faster than we could give them a recommendation.
What are your personal values?
My family always taught me that every person has his or her own place in the world and that one cannot feel at ease unless they “take their place.” For me, that means being a good mother, sister and daughter; a true and loyal friend; an honest, hardworking co-worker; and also it means conducting myself according the tenets of my own faith. Trying to balance all of that can be really hard sometimes, but I have learned that if I take an action that I believe in, even when it’s hard, eventually everything else falls into place.
What are your main interests outside of work?
For the longest time, all I wanted to do when I was done with work was be with my family. Now that my kids are getting older and following their own pursuits, I am starting to enjoy trying out new things. I’m learning that I really like yoga, while boot-camp style classes aren’t my thing. And I love being on the water, though I like being in a kayak much more than I like being in a sail boat.
Thank you for taking the time to talk to us and sharing your journey.
Cytel employees are active and well regarded in industry associations and communities around the world. Would you like to join our talented team? We have roles for biostatisticians, statistical programmers, and data managers at all levels across our global locations. To find out more about rewarding careers with us click below.