Insights on Quantitative Systems Pharmacology with Piet van der Graaf

Insights on Quantitative Systems Pharmacology with Piet van der Graaf

Quantitative systems pharmacology (QSP) is an emerging biosimulation technology that is going to increase pharmaceutical R&D productivity. This week at the Roundtable, we’re talking with Dr. Piet van der Graaf, PharmD, PhD about QSP and his vision for how it supports meeting the goal of precision medicine. Dr. van der Graaf is a professor of systems pharmacology, chair of pharmacology, and director of the Leiden Academic Centre for Drug Research at Leiden University in the Netherlands. He is also a former director of XenoloqiQ, the UK-based QSP consultancy, which Certara just acquired. Dr. van der Graaf is now the vice president of QSP at Certara.

My Trends in Quantitative Systems Pharmacology Q&A with Dr. van der Graaf

Q. What are the biggest pharmaceutical industry challenges that QSP can help address?

A. One of the biggest challenges – and, hence, the biggest opportunity for QSP – is drug attrition in Phase 2 clinical trials. Investigational medicines get tested for the first time in patients in phase 2 clinical trials. This is the point when many drug programs fail. In fact, approximately 80 percent of new drugs that move into Phase 2 fail. The major reason for this failure is that the drug doesn’t show efficacy. This high failure rate wastes lots of money and time.

So, that’s a big challenge that pharma’s wrestling with. Using QSP to augment current biosimulation technology (modeling and simulation) could help tackle this issue.

Q. How could QSP help solve the problem of high attrition rates in Phase 2 studies?

A. A lot of the Phase 2 failures may be due to targeting the wrong mechanism or patient population or to suboptimal dosing. The drug’s molecular target might be correct, but the sponsor didn’t get the dose right. Or perhaps, the sponsor should have considered a combination therapy approach instead of a single target therapy.

All of these elements can be addressed by QSP years before the pivotal Phase 2 trial. Being armed with this knowledge would enable sponsors to change their Phase 2 strategy regarding dose or dosing frequency, or drug combinations well before the actual trials. Insights from QSP would also help them to influence the trial designs and/or which patient populations to target.

Precision medicine is a major initiative in the industry. In the past, we treated many diseases as monolithic. We used a “one size fits all” approach for everyone. We’ve now started to recognize that many diseases are actually a plethora of different diseases affecting distinct subpopulations of patients. By leveraging QSP, sponsors can rationally plan which patient subpopulation to target before running that make-or-break Phase 2 trial. That could make the difference between failure and success in Phase 2.

Q. How is QSP a new way of tackling these problems? How is it distinct from other biosimulation approaches such as physiologically-based pharmacokinetic (PBPK) modeling and simulation?

A. Biosimulation as applied to pharmacokinetics has had a fantastic track record. For example, the Simcyp Simulator performs trials in virtual patients to predict and understand pharmacokinetics in various clinical scenarios and subpopulations.

Now, we’re starting to fill the gap between the early-stage understanding of pharmacokinetics and the late-stage understanding of drug efficacy. QSP will help fill this void to address the problem of high attrition in Phase 2 trials. By using QSP to better understand efficacy and safety in clinical studies, we will be able to help pharma to become more successful in R&D.

Q. Piet, I’ve heard you discuss the notion of “three pillars” of drug-receptor binding and cell signaling modulation. Can you elaborate on this concept for our readers?

A. Absolutely. This concept came out of my tenure at Pfizer. Three pillars form the basis for a successful Phase 2 trial. Pillar 1 is target exposure— the exposure of the drug at the site of action. That’s the territory of PBPK modeling: using biosimulation modeling to predict and understand drug exposure at the site of action.

The second pillar comes into play once you have exposure at the site of action. Now, the drug needs to engage with its target. The drug must bind the target, and it needs to do that in the right way.

The third pillar focuses on what happens once the drug binds its target with reasonable affinity. At this point, the drug needs to express the right pharmacology. To generate the desired biological response, the drug needs to activate a particular molecular signaling pathway (pharmacological agonism) or it needs to stop that particular pathway (pharmacological antagonism). The three pillars concept has been adopted industry-wide to explain the causes of Phase 2 attrition. My focus is using QSP to expand the strength previously developed for Pillar 1 into the Pillar 2 and 3 concepts.

Q. What are the major challenges facing the QSP field? What do you recommend to tackle these challenges?

A. A lot of the challenges with QSP stem from it being a relatively new discipline. The pharmaceutical industry has only been investing in QSP for the past five years or so. As a field, QSP got its start through initiatives at the U.S. National Institutes of Health. Systems biologists and pharmacometricians got together and asked, “How can we create synergy between our fields? Will linking these two disciplines help us to tackle this Phase 2 failure issue?”

QSP has gained significant interest in the pharmaceutical industry. In particular, pharma looks to QSP to utilize the tremendous amount of data now being generated from the “omics sciences” (genomics, proteomics, and metabolomics). Today, we are bombarded by mountains of data that we have only been able to generate within the last few years. Using QSP models and other biosimulation tools will help integrate all that new data into pharmaceutical R&D to support the discovery of new medicines.

Q. What sort of drug development questions can QSP help answer?

A. That’s a very good question. PBPK models answer questions such as “How much drug exposure do I get at the site of action?” Going back to the pillar concept, this question falls under Pillar I. It’s also important to be able to understand drug exposure in sensitive populations such as the elderly and children. Again, PBPK models can quantify how drug exposure might different in healthy volunteers vs. patients.

QSP builds on these insights gained from PBPK. Once we know how much drug is at the site of action, how will it modulate cellular signaling to exert a pharmacological effect? What pharmacological action will it have at that particular organ? Answering these questions will provide insight into the mechanisms of drug efficacy.

At the same time, QSP can also shed light on drug toxicity. For example, perhaps I’m studying a drug that acts on the brain. Since the drug is systemically available, it will also get to other organs, including the liver. I can use systems pharmacology methods to translate the exposure in the liver to a potential side effect. QSP translates pharmacokinetics (drug exposure) to pharmacodynamics (pharmacological effects).

Q. Which therapeutic areas does QSP benefit the most?

A. In principle, QSP can be applied to any disease area. However, QSP approaches can be applied immediately to a few areas with the greatest impact. The biggest, immediate impact would be in areas where pharma is focusing on at the moment. So, that’s oncology and, in particular, immuno-oncology, which is arguably the hottest area in pharmaceutical research now. The second therapeutic area that comes to mind is immunology — indications like arthritis. The third area would be cardiovascular and metabolic diseases like diabetes. And finally, QSP will influence central nervous system indications including Alzheimer’s, schizophrenia, and Parkinson’s disease. These are the main disease areas where QSP could influence pharma R&D. At the same time, QSP can also impact smaller therapeutic areas and rare disease drug development. In particular, QSP will inform drug programs for rare diseases where the biology is well understood. Finally, QSP will provide insight on drug safety and toxicity issues which span across indications and diseases.

Q. As you mentioned earlier, QSP is a young discipline. What steps must be taken for QSP to realize its full potential?

A. Most pharma companies recognize the potential of QSP. They all understand the impact it may have on their pharmaceutical R&D output, particularly regarding Phase 2.

But, sponsors also recognize that building a QSP program alone may not be the best way to achieve success. Building these models is a big effort! Most individual companies are not in the position to take on this effort. Also, successful QSP models require pulling together massive amounts of data. I think that many companies recognize that this could be done better and more efficiently by working together.

To take QSP to the next level, we also need a QSP infrastructure, i.e. software and IT tools, that both companies and regulators can use and understand. Without an infrastructure, converting QSP from a purely academic discipline into one that industry actually uses will be very difficult.

Q. What is the regulatory perspective on QSP?BLOG_Insights-on-Quantitative-Systems-Pharmacology-with-Piet-van-der-Graaf_2

A. Where QSP is now regarding regulatory acceptance is analogous to the status of PBPK a decade ago. Today, regulators have embraced PBPK as a both a discipline and as an integral part of company filings. I aspire to take QSP to that level. That’s a big challenge, and we certainly can’t claim that QSP has anywhere near the degree of regulatory currency that PBPK has at the moment. Working with the team of dedicated and talented scientists at Certara, we can play an important role in achieving this goal.

Recent events suggest that the regulatory climate for QSP is moving in the right direction. A year ago, the FDA published on its website for the first time, the use of a quantitative systems pharmacology model which connected bone turnover markers with bone mineral density. Using this QSP model led the FDA to propose a different dosing regimen for a biologic from what the sponsor had proposed. I predict that this is the first of many cases that will reap the benefits of using QSP to help deliver better treatments to patients.

 Chatting with a thought leader in QSP

I’d like to thank Dr. van der Graaf for pulling up a seat at the Roundtable and sharing his insights with us.

To learn more about how QSP and other types of biosimulation technology inform drug development and labeling, read our white paper, “Drug Label Optimization Using Proven Biosimulation Methodology.” Also, please remember to subscribe to this blog to get each week’s dose of insights delivered straight to your inbox!

Suzanne Minton

About the Author

Suzanne Minton

Scientific Communications Manager, Certara

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Dr. Suzanne Minton is the scientific communications manager at Certara. She helps develop the science-focused, value-oriented content that our customers go wild for. When she's not writing about the hottest problems in drug development, Suzanne enjoys spending time with her husband and two young children.