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6 Real-life Lessons About PBPK Modeling

I recently had the pleasure of attending a 1.5 day Certara forum for management on the applications of physiologically-based pharmacokinetic (PBPK) modeling and simulation in Chicago, IL. Our CSO Dr. Amin Rostami and Certara consulting scientist, Dr. Alice Ke aptly led the forum. The highlight of the meeting was discussing the latest challenges and trends in the industry with my colleagues and the attendees. In this blog post, I’ll discuss some of the takeaways regarding how PBPK supports drug development.

  1. PBPK has a long history: A typical view of PBPK is that it describes the concentration-time profile of a drug based on physiological knowledge of blood flow through organs and drug partitioning. Indeed, Theorell proposed the first PBPK model in 1937. Three advancements have enabled PBPK to become the powerful tool that it is today. First, modern computing power supports the vast number of calculations that these models require. Second, PBPK has been joined to IVIVE (in vitro in vivo extrapolation) of ADME (absorption, distribution, metabolism, excretion). Put more simply, in vitro assays of human enzymes, cells, and tissues can be used to predict the behavior of drugs at the organ level. Finally, the original PBPK framework used only whole organs. Updated PBPK models now include expanded models that detail the transit of drug through the intestine, liver, kidney, brain, and lung. In short, “new PBPK” is not the same as the classical concept of PBPK.
  2. The Simcyp Simulator is a “black box”: This is only true if you have never opened the box! Simcyp workshops and publications help educate the scientific community on the theoretical and practical aspects of using the Simcyp Simulator. Since 1999, the Simcyp group has published 117 articles in peer-reviewed journals. In recent years, we have tried to remove some of the barriers to attending the workshops including holding them in China and conducting them in Japanese.
  3. The issue of how to communicate simulated results in drug labels: The use of modeling and simulation to support claims on drug labels has become commonplace. So, it’s no longer a matter of IF drug labels will include claims based on evidence from in silico trials, but rather HOW this information will be communicated. At this year’s ASCPT meeting, a Roundtable titled “How Should Simulated DDI Results be Communicated in the Label” was held. Two schools of thought exist for the question of whether drug-drug interaction (DDI) results should contain a “disclaimer” that they were obtained from simulations rather than clinical trials. One posits that clinicians should have this information to support their decision making. The other view is that the predictions derived from robust in silico models should have the same validity as those from clinical trials, and identifying their source dilutes their significance. We will have to wait and see how this issue will be resolved.
  4. Only in silico studies can assess the entire spectrum of patient variability: The FDA requires that drug studies be conducted in certain special populations (patients with renal or hepatic impairment, pregnant women, pediatrics, and patients taking concomitant drugs). However, there are many other special populations that are worth considering: different ethnic groups, obese and morbidly obese patients, post-bariatric surgery patients, etc. Randomized clinical trials only assess a small fraction of potential PK/PD variability. By contrast, in silico studies can test a virtually unlimited set of “what if?” scenarios.
  5. Regardless of the model, personality and leadership matter! We can get so wrapped up with the nuances of a model that we risk forgetting that models are built by people whose attitudes and skills matter. Successful modeling projects contain three critical elements: robust and appropriate input data, sound models, and input from modelers with the right expertise. The ultimate goal of modeling isn’t merely generating results but rather having an impact on the drug label and/or regulatory decisions.
  6. Good science pays off: At the end of the day, it’s important to reflect on why we are in this business in the first place. George Merck said it best,

    “We try never to forget that medicine is for the people. It is not for the profits. The profits follow, and if we have remembered that, they have never failed to appear. The better we have remembered it, the larger they have been.”

    PBPK modeling technology offers a tremendous opportunity to help bring safer, more effective treatments to patients and help make the promise of “personalized medicine” one step closer to reality.

pubmed searches for PBPK over time

Learn more about how modeling and simulation is revolutionizing drug development

Today, drug development is carried out in human subjects and animals. However, as computing power and the number of sophisticated technology platforms grow exponentially, and our knowledge of human health and disease increases, the virtualization of clinical research and development will grow steadily. Our CEO, Dr. Edmundo Muniz recently wrote an article on this topic for Clinical Researcher. I hope that you’ll read it and let me know what you think in the comments section. How do you see the virtualization of R&D impacting drug development?

About the author

By: Suzanne Minton

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