Understanding PK Data
Module goal: Build intuition for PK data before modeling—understand what you are actually looking at, what is visible, and what is hidden.
Learning Objectives
By the end of this module, you will be able to:
- Explain what concentration–time data represents.
- Distinguish between observed data and the underlying biological process.
- Understand how dose and sampling design influence what can be observed.
- Recognize different sources of variability in PK data.
- Interpret individual profiles before relying on summaries.
Why This Module Matters
Before you can model anything, you need to understand:
- what the data actually represents
- what parts of the system are observable
- what conclusions are supported (or not supported)
Many modeling mistakes come from misunderstanding the data—not the model.
If you misinterpret the data, even a correct model will lead to the wrong conclusions.
PK Data Mindset
When you look at a PK dataset, always ask:
- What is observed vs what is inferred?
- What parts of the profile are visible?
- What parts are missing?
- What variability is present and why?
This mindset is the foundation of all pharmacometric reasoning.
Lessons in This Module
Work through these in order:
What Concentration–Time Data Represents
Understand what each data point means and why profiles are only partial views of the system.Dose, Exposure, and Sampling
Learn how dose enters the system and how sampling determines what you can actually observe.Variability in Observed PK Data
Identify sources of variability and understand why variability is central—not incidental—in PMx.
What You’ll Be Ready For After This Module
After completing this module, you should be able to:
- read and interpret PK profiles with confidence
- understand the limitations of observed data
- recognize when data are insufficient for certain conclusions
- move into profile shapes and compartments with strong intuition
This module bridges conceptual understanding (Module 1) and mechanistic intuition (next modules).