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Application of PK/PD Modeling and Simulation in Drug Discovery and Development in China

By Yuancheng Chen, Research Associate of Phase 1 Unit, Huashan Hospital, Fudan University, and visiting scholar of Uppsala University

Certara recently held its first virtual Phoenix User Group meeting in China. Over this three-day meeting, attendees learned from Certara’s experts on specific Phoenix workflow examples, updates to the Phoenix product roadmap, an overview the latest V8.3 release, and more. This blog is a summary of my keynote presentation. It discusses the need for and application of pharmacokinetic/pharmacodynamic (PK/PD) modeling and simulation in China.

Modeling and simulation of pharmacokinetics and pharmacodynamics is currently one of the most exciting interdisciplinary frontiers in the field of drug development. The goal of this blog is first to explain the fundamentals of pharmacokinetics – i.e., the study of how drugs are affected by the body, as well as the basics of the related field of pharmacodynamics – i.e., the study of how drugs affect the body. Then we will look in detail at how these two fields can be modeled and studied via simulation to help us better understand drug safety and efficacy in situations where direct clinical trials are not feasible.

PK/PD connects PK and PD via mathematical modeling and is used to describe a drug’s effects over time, which can be used to find factors affecting a drug’s effects and perform quantitative evaluations. Both PK and PD can be studied via a traditional approach based on empirical observation. We can directly measure changes in drug concentration in the body over time or directly observe how different physiological parameters evolve over time following drug administration. However, studies like this are both time-consuming and resource-intensive and may even be unfeasible. Modeling, on the other hand, offers a process by which mathematical solutions to real-world problems can be obtained. Modeling is useful for understanding complex system behavior, with applications including designing experimental systems, simulating existing systems, and obtaining more accurate theoretical solutions based on experimental results.

Similarly, simulation provides a faster and more cost-effective alternative to in vivo studies or clinical trials. Simulation refers to the virtualization of the real world to find out why past events occurred, or to predict future events. It can be particularly useful for conceptualizing different scenarios and observing the impact of these scenarios on system structure and behavior. Simulation can also be used to ascertain relationships between variables and explaining which variables are more important and how they affect other variables as well as the system as a whole.

Both modeling and simulation are frequently used in PK/PD analysis. On one hand, a model is useful for describing trends in how data change. Modeling can be used to obtain PK or PK/PD parameters. On the other hand, through simulation, it is possible to understand the effects of dose, frequency of administration and other factors (e.g., body weight) on drug concentration or efficacy. Using modeling, we can capture concentration-time and effect-concentration relationships while a simulation can be used to obtain dose duration and effect-time curves, providing useful information for optimizing dosing regimens that have not actually been tested.

There are many other advantages to PK/PD modeling and simulation studies. Certain special population-based clinical trials are more difficult to conduct (e.g., those involving children), and simulation studies based on extrapolating adult data to children are often encouraged. This approach makes it possible to simultaneously accelerate drug research and reduce research costs.

A variety of different PK/PD models available can be used when performing modeling, including PK models, physiological pharmacokinetic (PPK) models and physiologically-based pharmacokinetic (PBPK) models, and PD models (including Emax models, S-type Emax models, mechanism-based models (e.g., indirect effect models), as well as systematic pharmacological models). Drug companies have used these approaches to obtain FDA approval.

Regulators have a favorable view toward modeling and simulation studies. The Chinese regulatory agency, National Medical Products Administration (NMPA) encourages sponsors to use modeling and simulation in developing new drugs, especially in children. Best practices for PK/PD modeling and simulation studies are detailed in technical guidelines for clinical research on antineoplastic drugs, antituberculosis drugs and drugs used for treating acute heart failure.

Next, let’s take a closer look at different software solutions for PK/PD modeling and simulation. In the Phoenix WinNonlin software package, the modeling and simulation functions are embedded in the same module. It is possible to switch between modes by simply checking a box in the software interface.

More specifically, PK/PD modeling and simulations are performed via a five-step process:

  1. importing and processing data,
  2. choosing a PK or PD model from the model library or building a mechanism-based PK/PD model
  3. estimating parameters using nonlinear regression or nonlinear mixed-effects modeling methods
  4. exploring model dynamics using parametric scanning and sensitivity analysis techniques and
  5. simulating different drug delivery scenarios to test hypotheses.

The WinNonlin software provides multiple pharmacokinetic/pharmacodynamic analysis tools, and its graphical interface makes it particularly suitable for beginner modelers. In summary, modeling and simulation are powerful tools for drug discovery and development, reducing research costs and increasing research efficiency. To see Phoenix in action, request a demo now:

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